Computer and Computing Technologies in Agriculture IV, Part I - CCTA 2010

IFIP Advances in Information and Communication Technology 344 Editor-in-Chief A. Joe Turner, Seneca, SC, USA Editori...

Author: Daoliang Li | Yande Liu | Yingyi Chen

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IFIP Advances in Information and Communication Technology

344

Editor-in-Chief A. Joe Turner, Seneca, SC, USA

Editorial Board Foundations of Computer Science Mike Hinchey, Lero, Limerick, Ireland Software: Theory and Practice Bertrand Meyer, ETH Zurich, Switzerland Education Arthur Tatnall, Victoria University, Melbourne, Australia Information Technology Applications Ronald Waxman, EDA Standards Consulting, Beachwood, OH, USA Communication Systems Guy Leduc, Université de Liège, Belgium System Modeling and Optimization Jacques Henry, Université de Bordeaux, France Information Systems Jan Pries-Heje, Roskilde University, Denmark Relationship between Computers and Society Jackie Phahlamohlaka, CSIR, Pretoria, South Africa Computer Systems Technology Paolo Prinetto, Politecnico di Torino, Italy Security and Privacy Protection in Information Processing Systems Kai Rannenberg, Goethe University Frankfurt, Germany Artificial Intelligence Tharam Dillon, Curtin University, Bentley, Australia Human-Computer Interaction Annelise Mark Pejtersen, Center of Cognitive Systems Engineering, Denmark Entertainment Computing Ryohei Nakatsu, National University of Singapore

IFIP – The International Federation for Information Processing IFIP was founded in 1960 under the auspices of UNESCO, following the First World Computer Congress held in Paris the previous year. An umbrella organization for societies working in information processing, IFIP’s aim is two-fold: to support information processing within its member countries and to encourage technology transfer to developing nations. As its mission statement clearly states, IFIP’s mission is to be the leading, truly international, apolitical organization which encourages and assists in the development, exploitation and application of information technology for the benefit of all people. IFIP is a non-profitmaking organization, run almost solely by 2500 volunteers. It operates through a number of technical committees, which organize events and publications. IFIP’s events range from an international congress to local seminars, but the most important are: • The IFIP World Computer Congress, held every second year; • Open conferences; • Working conferences. The flagship event is the IFIP World Computer Congress, at which both invited and contributed papers are presented. Contributed papers are rigorously refereed and the rejection rate is high. As with the Congress, participation in the open conferences is open to all and papers may be invited or submitted. Again, submitted papers are stringently refereed. The working conferences are structured differently. They are usually run by a working group and attendance is small and by invitation only. Their purpose is to create an atmosphere conducive to innovation and development. Refereeing is less rigorous and papers are subjected to extensive group discussion. Publications arising from IFIP events vary. The papers presented at the IFIP World Computer Congress and at open conferences are published as conference proceedings, while the results of the working conferences are often published as collections of selected and edited papers. Any national society whose primary activity is in information may apply to become a full member of IFIP, although full membership is restricted to one society per country. Full members are entitled to vote at the annual General Assembly, National societies preferring a less committed involvement may apply for associate or corresponding membership. Associate members enjoy the same benefits as full members, but without voting rights. Corresponding members are not represented in IFIP bodies. Affiliated membership is open to non-national societies, and individual and honorary membership schemes are also offered.

Daoliang Li Yande Liu Yingyi Chen (Eds.)

Computer and Computing Technologies in Agriculture IV 4th IFIP TC 12 Conference, CCTA 2010 Nanchang, China, October 22-25, 2010 Selected Papers, Part I

13

Volume Editors Daoliang Li Yingyi Chen China Agricultural University EU-China Center for Information & Communication Technologies (CICTA) 17 Tsinghua East Road, Beijing, 100083, P.R. China E-mail: {dliangl, chenyingyi}@cau.edu.cn Yande Liu East China Jiaotong University College of Mechanical and Electronic Engineering Shuanggang Road, Nanchang, 330013 Jiangxi, China E-mail: [email protected]

ISSN 1868-4238 e-ISSN 1868-422X ISBN 978-3-642-18332-4 e-ISBN 978-3-642-18333-1 DOI 10.1007/978-3-642-18333-1 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2010942867 CR Subject Classification (1998): I.2.11, H.4, C.3, C.2, D.2, K.4.4

© IFIP International Federation for Information Processing 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

I want to express my sincere thanks to all authors who submitted research papers to the 4th IFIP International Conference on Computer and Computing Technologies in Agriculture and the 4th Symposium on Development of Rural Information (CCTA 2010) that were held in Nanchang, China, 22–25 October 2010. This conference was hosted by CICTA (EU-China Centre for Information & Communication Technologies, China Agricultural University); China Agricultural University; China Society of Agricultural Engineering, China; International Federation for Information Processing (TC12); Beijing Society for Information Technology in Agriculture, China. It was organized by East China Jiaotong University. CICTA focuses on research and development of advanced and practical technologies applied in agriculture and aims at promoting international communication and cooperation. Sustainable agriculture is currently the focus of the whole world, and the application of information technology in agriculture has become more and more important. ‘Informatized agriculture’ has been the goal of many countries recently in order to scientifically manage agriculture to achieve low costs and high income. The topics of CCTA 2010 covered a wide range of interesting theories and applications of information technology in agriculture, including simulation models and decision-support systems for agricultural production, agricultural product quality testing, traceability and e-commerce technology, the application of information and communication technology in agriculture, and universal information service technology and service systems development in rural areas. We selected 352 best papers among those submitted to CCTA 2010 for these proceedings. It is always exciting to have experts, professionals and scholars getting together with creative contributions and sharing inspiring ideas which will hopefully lead to great developments in these technologies. Finally, I would like also to express my sincere thanks to all the authors, speakers, session chairs and attendees for their active participation and support of this conference.

October 2010

Daoliang Li

Conference Organization

Organizer East China Jiaotong University

Organizing Committee Chair Yande Liu

Academic Committee Chair Daoliang Li

Conference Secretariat Lingling Gao

Sponsors China Agricultural University China Society of Agricultural Engineering, China International Federation for Information Processing, Austria Beijing Society for Information Technology in Agriculture, China National Natural Science Foundation of China

Table of Contents – Part I

3-D Turbulence Numerical Simulation for the Flow Field of Suction Cylinder-Seeder with Socket-Slots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanjun Zuo, Xu Ma, Long Qi, and Xinglong Liao An Architecture for the Agricultural Machinery Intelligent Scheduling in Cross-Regional Work Based on Cloud Computing and Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiguo Sun, Hui Xia, and Wensheng Wang A Comparative Study of Modified Materials of Acetylcholinesterase Biosensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xia Sun, Xiangyou Wang, Wenping Zhao, Shuyuan Du, Qingqing Li, and Xiangbo Han A Detection Method of Rice Process Quality Based on the Color and BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Wan, Changjiang Long, and Xiaomao Huang

1

9

16

25

A Digital Management System of Cow Diseases on Dairy Farm . . . . . . . . Lin Li, Hongbin Wang, Yong Yang, Jianbin He, Jing Dong, and Honggang Fan

35

A General Agriculture Mobile Service Platform . . . . . . . . . . . . . . . . . . . . . . Haiyan Hu and Xiaolu Su

41

A Halal and Quality Attributes Driven Animal Products Formal Producing System Based on HQESPNM . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Han and Wenxing Bao

48

A Metadata Based Agricultural Universal Scientific and Technical Information Fusion and Service Framework . . . . . . . . . . . . . . . . . . . . . . . . . . Cui Yunpeng, Liu Shihong, Sun SuFen, Zhang Junfeng, and Zheng Huaiguo A Method to Calibrate the Electromagnetic Tracking Instrument When Measuring Branches of Fruit Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ding-Feng Wu, Jian Wang, Guo-Min Zhou, and Li-Bo Liu

56

62

A Method of Deduplication for Data Remote Backup . . . . . . . . . . . . . . . . . Jingyu Liu, Yu-an Tan, Yuanzhang Li, Xuelan Zhang, and Zexiang Zhou

68

A Localization Algorithm for Sparse-Anchored WSN in Agriculture . . . . Chunjiang Zhao, Shufeng Wang, Kaiyi Wang, Zhongqiang Liu, Feng Yang, and Xiandi Zhang

76

VIII


A New Method of Transductive SVM-Based Network Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manfu Yan and Zhifang Liu

87

Design and Simulation of Jujube Sapling Transplanter . . . . . . . . . . . . . . . . Wangyuan Zong, Wei Wang, Yonghua Sun, and Hong Zhang

96

A Precision Subsidy Management System for Strawberry Planting in ChangPing Distinct of BeiJing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chi Zhang, Tian’en Chen, and Liping Chen

103

A Semantic Search Engine Based on SKOS Model Ontology in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yong Yang, Jinhui Xiong, and Shuyan Wang

110

A System for Detection and Recognition of Pests in Stored-Grain Based on Video Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Yang, Bo Peng, and Jianqin Wang

119

A Tabu Search Approach to Fuzzy Optimization of Camellia Oleifera Fertilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qin Song, Fukuan Zhao, and Yujun Zheng

125

AgOnt: Ontology for Agriculture Internet of Things . . . . . . . . . . . . . . . . . . Siquan Hu, Haiou Wang, Chundong She, and Junfeng Wang

131

Auto Recognition of Navigation Path for Harvest Robot Based on Machine Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bei He, Gang Liu, Ying Ji, Yongsheng Si, and Rui Gao

138

An Agricultural Tri-dimensional Pollution Data Management Platform Based on DNDC Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lihua Jiang, Wensheng Wang, Xiaorong Yang, Nengfu Xie, and Youping Cheng

149

An Analysis on the Inter-annual Spatial and Temporal Variation of the Water Table Depth and Salinity in Hetao Irrigation District, Inner Mongolia, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Du, Peiling Yang, Yunkai Li, Shumei Ren, Xianyue Li, Yandong Xue, Lingyan Wang, and Wei Zhao

155

An Efficient and Fast Algorithm for Mining Frequent Patterns on Multiple Biosequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Liu and Ling Chen

178

An Inspection Method of Rice Milling Degree Based on Machine Vision and Gray-Gradient Co-occurrence Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Wan and Changjiang Long

195


IX

An Intelligent Retrieval Platform for Distributional Agriculture Science and Technology Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaorong Yang, Wensheng Wang, Qingtian Zeng, and Nengfu Xie

203

Analysis of Factors Influencing the Off-Farm Employment Based on the Method of PLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Huang and Yizong Xu

210

Analysis of Income Difference among Rural Residents in China . . . . . . . . Yan Xue, Yeping Zhu, and Shijuan Li

219

Analysis of Secretary Proteins in the Genome of the Plant Pathogenic Fungus Botrytis Cinerea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yue Zhang, Jing Yang, Lin Liu, Yuan Su, Ling Xu, Youyong Zhu, and Chengyun Li Analysis of the Heat Transfer Performance of Vapor-Condenser during Vacuum Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gailian Li, Tingxiang Jin, and Chunxia Hu Analysis on Dynamic Characteristics of Landscape Patterns in Hailer and around Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongbin Zhang, Guixia Yang, Qing Huang, Gang Li, Baorui Chen, and Xiaoping Xin

227

238

250

Application and Demonstration of Digital Maize Planting and Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shijuan Li and Yeping Zhu

261

Implement of Fuzzy Control for Greenhouse Irrigation . . . . . . . . . . . . . . . . Wenttao Ren, Quanli Xiang, Yi Yang, Hongguang Cui, and Lili Dai

267

Application of Background Information Database in Drought Monitoring of Guangxi in 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Yang, Weiping Lu, Chaohui Wu, Yuhong Li, and Shiquan Zhong

275

Application of Fuzzy Clustering Analysis in Classification of Soil in Qinghai and Heilongjiang of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ping Han, Jihua Wang, Zhihong Ma, Anxiang Lu, Miao Gao, and Ligang Pan Application of Molecular Imprinting Technique in Organophosphorus Pesticides Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liu Zhao, Hua Ping, Ling Xiang, Ping Han, Jihua Wang, and Ligang Pan Assessing Rice Chlorophyll Content with Vegetation Indices from Hyperspectral Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xingang Xu, Xiaohe Gu, Xiaoyu Song, Cunjun Li, and Wenjiang Huang

282

290

296

X


Automated Extracting Tree Crown from Quickbird Stand Image . . . . . . . Guang Deng, Zengyuan Li, Honggan Wu, and Xu Zhang

304

Bayesian Networks Modeling for Crop Diseases . . . . . . . . . . . . . . . . . . . . . . Chunguang Bi and Guifen Chen

312

Characteristics of Soil Environment Variation in Oasis–Desert Ecotone in the Process of Oasis Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haifeng Li, Fanjiang Zeng, Dongwei Gui, and Jiaqiang Lei

321

Chlorimuronethyl Resistance Selectable Marker Unsuited for the Transformation of Rice Blast Fungus (Magnaporthe Grisea) . . . . . . . . . . . Chang Qing, Yang Jing, Liu Lin, Su Yuan, Li Jinbin, Zhu Youyong, and Li Chengyun Support Vector Machine to Monitor Greenhouse Plant with Gaussian Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manfu Yan, Qing Zhang, and Jianhang Zhang Classification Methods of Remote Sensing Image Based on Decision Tree Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lihua Jiang, Wensheng Wang, Xiaorong Yang, Nengfu Xie, and Youping Cheng

335

343

353

Computer-Aided Design System Development of Fixed Water Distribution of Pipe Irrigation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingyao Zhou, Susheng Wang, Zhen Zhang, and Lidong Chen

359

Construction and Practice of Information Demonstration Area in Mentougou District of Beijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juan Pan, Na Zhang, Shan Yao, and Jian Xu

367

Data Acquisition Method for Measuring Mycelium Growth of Microorganism with GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juan Yang, Jingyin Zhao, Qian Guo, Yunsheng Wang, and Ruijuan Wang Decision Support System for Quantitative Calculation of Crop Climatic Suitability in Hebei Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Zhang, Youfei Zheng, and Xin Wang Delineation of Suitable Areas for Maize in China and Evaluation of Application for the Technique of Whole Plastic-Film Mulching on Double Ridges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chaojie Jia, Wenlong Zhao, Yaxiong Chen, and Guojun Sun DEM Simulation and Analysis of Seeds Supply by the Vibrating Seed Box of Magnetic Cylinder Seeder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiuping Shao, Jianping Hu, Yingsa Huang, and Fa Liu

374

381

390

401


XI

Design and Experiment of Onboard Field 3D Topography Surveying System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingming Guo, Gang Liu, and Xinlei Li

409

Implementation of Agro-environmental Information Service System Based on WebGIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lin Peng, Linnan Yang, and Limin Zhang

417

Design and Implementation of Automatic Control System for Rice Seed Tape Winding Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongguang Cui, Wentao Ren, Benhua Zhang, Yi Yang, Lili Dai, and Quanli Xiang

428

Design and Implementation of Crop Potential Model System Based on GIS and Componentware Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hao Zhang, Li Ding, Guang Zheng, Xin Xu, Lei Xi, and Xinming Ma

437

Design and Realization of a VRGIS-Based Digital Agricultural Region Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaojun Liu, Yuou Zhang, Weixing Cao, and Yan Zhu

446

Design and Simulation Analysis of Transplanter’s Planting Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fa Liu, Jianping Hu, Yingsa Huang, Xiuping Shao, and Wenqin Ding

456

Design and Simulation for Bionic Mechanical Arm in Jujube Transplanter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yonghua Sun, Wei Wang, Wangyuan Zong, and Hong Zhang

464

Design for Real-Time Monitoring System of High Oxygen Modified Atmosphere Box of Vegetable and Fruit for Preservation . . . . . . . . . . . . . . Zhanli Liu, Congcong Yan, Xiangyou Wang, and Xiangbo Han

472

Design of Agent-Based Agricultural Product Quality Control System . . . Yeping Zhu, Shijuan Li, Shengping Liu, and E. Yue

476

Design of ETL Process on Spatio-temporal Data and Study of Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Buyu Wang, Changyou Li, Xueliang Fu, Meian Li, Dongqing Wang, Huibin Du, and Yajuan Xing

487

Design of Fuzzy Drip Irrigation Control System Based on ZigBee Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinjian Xiang

495

Design of Greenhouse Environmental Parameters Prediction System . . . . Haokun Zhang and Heru Xue

502

XII


Design of Limb for Parallel Mechanism Based on Screw Theory . . . . . . . . Zhigang Lai, Lixin Li, and Ping’ an Liu

508

Design of Non-full Irrigation Management Information System of Hebei Province Based on GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junliang He, Yanxia Zheng, and Shuyuan Zhang

519

The Monitoring System of Water Environment Based on Overlay Network Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xueliang Fu, Changyou Li, Buyu Wang, Honghui Li, Hailei Ma, and Dongnan Zhu

526

Design of Rotary Root Stubble Digging Machine Based on Solidworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinglong Liao, Xu Ma, and Yanjun Zuo

532

Design of the Network Platform Scheme Based on Comprehensive Information Sharing of Zigong City’s Characteristic Agriculture . . . . . . . . Wen Lei, Hong Zhang, and Lecai Cai

539

Detection of Surface Defects of Fruits Based on Fractal Dimension . . . . . Yongxiang Sun, Yong Liang, and Qiulan Wu

547

Detection Technology for Precision Metering Performance of Magnetic-Type Seeder Based on Machine Vision . . . . . . . . . . . . . . . . . . . . . Deyong Yang, Jianping Hu, and Zuqing Xie

555

Determination of Cr, Zn, As and Pb in Soil by X-Ray Fluorescence Spectrometry Based on a Partial Least Square Regression Model . . . . . . . Anxiang Lu, Xiangyang Qin, Jihua Wang, Jiang Sun, Dazhou Zhu, and Ligang Pan Determination of Thermal Conductivity of Aloe in the Cooling and Thawing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Zhang, Huizhong Zhao, Zhiyou Zhong, Jianhua Chen, Zhenhua Che, Jiahua Lu, and Le Yang Development and Application of Computer Assisted Breeding System in Rabbit Breeding Farm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xibo Qiao, Hongchao Wu, Suping Sun, Mingyong Li, Zhaopeng Wang, Jingui Dong, and Xinzhong Fan Development of a Web-based Information Service Platform for Protected Crop Pests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chong Huang and Haiguang Wang Development of Dairy Cattle Registration and Herd Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongchao Wu, Xibo Qiao, Xin Luan, Biao Li, Zhongle Chang, Jinghe Tan, and Xinzhong Fan

563

569

576

582

590


Development of the Information Management System for Monitoring Alien Invasive Species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui Li, Ningning Ge, Lingwang Gao, Zuorui Shen, Guoliang Zhang, Zhiyuan Zang, and Yi Li

XIII

594

Discriminate of Moldy Chestnut Based on Near Infrared Spectroscopy and Feature Extraction by Fourier Transform . . . . . . . . . . . . . . . . . . . . . . . . Zhu Zhou, Xiaoyu Li, Peiwu Li, Yun Gao, Jie Liu, and Wei Wang

600

Discrimination of Ca, Cu, Fe, and Na in Gannan Navel Orange by Laser Induced Breakdown Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yao Mingyin, Lin Jinlong, Liu Muhua, Li Qiulian, and Lei Zejian

608

Dynamic Modeling on Nitrogen Assignment in Tobacco . . . . . . . . . . . . . . . Peng Zhao, Yuanyuan Shi, Xinming Ma, and Shuping Xiong

614

Dynamic Study of Farmers’ Information Adoption in China . . . . . . . . . . . Jingjing Zhang, Lu Liu, Jian Zhang, and Jinyou Hu

623

Estimation of the Number of Apples in Color Images Recorded in Orchards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oded Cohen, Raphael Linker, and Amos Naor

630

Impact of Hydraulic Conductivity on Solute Transport in Highly Heterogeneous Aquifer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaili Wang and Guanhua Huang

643

Effects of Different Physical Characteristics on the Compression Molding Quality of Dried Fish Floss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongmei Xu, Li Zong, and Shengfa Yuan

656

Electronic Agriculture Resources and Agriculture Industrialization Support Information Service Platform Structure and Implementation . . . Xiaoming Zhao

669

Evaluation on the Agricultural Website’s Efficiency Based on DEA Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shangmin Deng and Weili Men

674

Examination Method and Implementation for Field Survey Data of Crop Types Based on Multi-resolution Satellite Images . . . . . . . . . . . . . . . Yang Liu, Mingyi Du, and Wenquan Zhu

681

Experimental Study of the Parameters of High Pulsed Electrical Field Pretreatment to Fruits and Vegetables in Vacuum Freeze-Drying . . . . . . . Yali Wu and Yuming Guo

691

Experimental Study on the Effects of Compression Parameters on Molding Quality of Dried Fish Floss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongmei Xu, Li Zong, Ling Li, and Jing Zhang

698

XIV


Extraction of Remote Sensing Information of LONGAN Under Support of “3S” Technology in Guangxi Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Yang, Chaohui Wu, Weiping Lu, Yuhong Li, and Shiquan Zhong

711

Farmland Irrigation Remote Monitoring System Based on Configuration Software and Multiple Serial Port Communication . . . . . . . . . . . . . . . . . . . Xiangbo Han and Zhanli Liu

717

Fast Discrimination of Mature Vinegar Varieties with Visible NIR Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanru Zhao, Shujuan Zhang, Huamin Zhao, Haihong Zhang, and Zhipeng Liu

721

Discrimination between Mature Vinegars of Different Geographical Origins by NIRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huishan Lu, Zhengguang An, Huanyu Jiang, and Yibin Ying

729

Prediction of Marked Age of Mature Vinegar Based on Fourier Transform Near Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhengguang An, Huishan Lu, Huanyu Jiang, and Yibin Ying

737

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

745

Table of Contents – Part II

Food Safety and Technological Implications of Food Traceability Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hailiang Zhang, Xudong Sun, and Yande Liu

1

Function Design of Township Enterprise Online Approval System . . . . . . Peng Lu, Gang Lu, and Chao Ding

11

Application of GPS on Power System Operation . . . . . . . . . . . . . . . . . . . . . Chunmei Pei, Huiling Guo, Xiuqing Yang, Bin He, Wei Liu, and Xuemei Li

18

Greenhouse Temperature Monitoring System Based on Labview . . . . . . . . Zhihong Zheng, Kai Zhang, and Chengliang Liu

23

Image-Driven Panel Design via Feature-Preserving Mesh Deformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baojun Li, Xiuping Liu, Yanqi Liu, Ping Hu, Mingzeng Liu, and Changsheng Wang

30

Influences of Temperature of Vapour-Condenser and Pressure in the Vacuum Chamber on the Cooling Rate during Vacuum Cooling . . . . . . . . Tingxiang Jin, Gailian Li, and Chunxia Hu

41

Inspection of Lettuce Water Stress Based on Multi-sensor Information Fusion Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongyan Gao, Hanping Mao, and Xiaodong Zhang

53

Measurement of Chili Pepper Plants Size Based on Mathematical Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yun Gao, Xiaoyu Li, Kun Qi, and Hong Chen

61

Methodology Comparison for Effective LAI Retrieving Based on Digital Hemispherical Photograph in Rice Canopy . . . . . . . . . . . . . . . . . . . . . . . . . . Lianqing Zhou, Guiying Pan, and Zhou Shi

71

Molecular Methods of Studying Microbial Diversity in Soil Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liu Zhao, Zhihong Ma, Yunxia Luan, Anxiang Lu, Jihua Wang, and Ligang Pan Monitoring the Plant Density of Cotton with Remotely Sensed Data . . . Junhua Bai, Jing Li, and Shaokun Li

83

90

XVI


Motion Blurring Direction Identification Based on Second-Order Difference Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junxiong Zhang, Fen He, and Wei Li

102

Multi-agent Quality of Bee Products Traceability Model Based on Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yue E, YePing Zhu, and YongSheng Cao

110

NIR Spectroscopy Identification of Persimmon Varieties Based on PCA-SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shujuan Zhang, Dengfei Jie, and Haihong Zhang

118

One Method for Batch DHI Data Import into SQL-Server: A Batch Data Import Technique for DateSet Based on .NET . . . . . . . . . . . . . . . . . . Liang Shi and Wenxing Bao

124

Optimal Sizing Design for Hybrid Renewable Energy Systems in Rural Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Fu, Jianhua Yang, and Tingting Zuo

131

Overall Layout Design of Iron and Steel Plants Based on SLP Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ermin Zhou, Kelou Chen, and Yanrong Zhang

139

Performance Forecasting of Piston Element in Motorcycle Engine Based on BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rong Dai

148

Performance Monitoring System for Precision Planter Based on MSP430-CT171 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lianming Xia, Xiangyou Wang, Duanyang Geng, and Qingfeng Zhang

158

Pervasive Agricultural Environment Monitoring System Based on Embedded Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hu Zhao, Sangen Wang, and Dake Wu

166

Precipitation Resource Potential in Mountainous Areas in Hebei Province Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Liu, Yanxia Zheng, and Zhiyong Zhao

177

Precision Drip Irrigation on Hot Pepper in Arid Northwest China Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huiying Yang, Haijun Liu, Yan Li, Guanhua Huang, and Fengxin Wang Study on Thermal Conductivities Prediction for Apple Fruit Juice by Using Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Zhang, Zhenhua Che, Jiahua Lu, Huizhong Zhao, Jianhua Chen, Zhiyou Zhong, and Le Yang

185

198


XVII

Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1, λ, ρ Model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Di-yi Chen, Yu-xiao Liu, Xiao-yi Ma, and Yan Long

205

Prediction of Irrigation Security of Reclaimed Water Storage in Winter Based on ANN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinfeng Deng

211

Progress of China Agricultural Information Technology Research and Applications Based on Registered Agricultural Software Packages . . . . . . Kaimeng Sun

218

Quantification Research on Different Load Weight-Bearing Running Biochemical Indexes of Rats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaping Shang

227

Rapid Determination of Ascorbic Acid in Fresh Vegetables and Fruits with Electrochemically Treated Screen-Printed Carbon Electrodes . . . . . . Ling Xiang, Hua Ping, Liu Zhao, Zhihong Ma, and Ligang Pan

234

Regional Drought Monitoring and Analyzing Using MODIS Data—A Case Study in Yunnan Province . . . . . . . . . . . . . . . . . . . . . . . . . . . Guoyin Cai, Mingyi Du, and Yang Liu

243

Regression Analysis and Indoor Air Temperature Model of Greenhouse in Northern Dry and Cold Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ting Zhao and Heru Xue

252

Remote Control System Based on Compressed Image . . . . . . . . . . . . . . . . . Weichuan Liao

259

Analysis of the Poverty-Stricken Rural Areas’ Demand for Rapid Dissemination of Agricultural Information—Taking Wanquan County in Hebei Province as an Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxia Shi and Yongchang Wu

264

Research and Analysis about System of Digital Agriculture Based on a Network Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Duan Yane

274

Research and Development of Preceding-Evaluation System of Rural Drinking Water Safety Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lian He and Jilin Cheng

283

Research of Evaluation on Cultivated Land Fertility in Xinjiang Desert Oasis Based on GIS Technology—Taking No. 22 State Farm as the Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling Wang, Xin Lv, and Hailong Liu

290

XVIII


Research of Pest Diagnosis System Development Tools Based on Binary Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yun Qiu and Guomin Zhou

300

Research of Soil Moisture Content Forecast Model Based on Genetic Algorithm BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caojun Huang, Lin Li, Souhua Ren, and Zhisheng Zhou

309

Research of the Measurement on Palmitic Acid in Edible Oils by Near-Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui Li, Jingzhu Wu, and Cuiling Liu

317

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ran Cao, Yushu Yang, and Wei Guo

322

Research on Docking of Supply and Demand of Rural Informationization and “Internet Digital Divide” in Urban and Rural Areas in China . . . . . . Zhongwei Sun, Yang Wang, and Peng Lu

329

Research on Evaluation of Rural Highway Construction in Hebei Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guisheng Rao, Limeng Qi, Runqing Zhang, and Li Deng

339

Research on Farmland Information Collecting and Processing Technology Based on DGPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weidong Zhuang and Chun Wang

345

Research on Fertilizer Efficiency of Continuous Cropping Greenhouse Cucumber Based on DEA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaohui Yang, Yuxiang Huang, Shuqin Li, and Sheng Huang

351

Design and Implementation of Crop Recommendation Fertilization Decision System Based on WEBGIS at Village Scale . . . . . . . . . . . . . . . . . Hao Zhang, Li Zhang, Yanna Ren, Juan Zhang, Xin Xu, Xinming Ma, and Zhongmin Lu

357

Research on Influenced Factors about Routing Selection Scheme in Agricultural Machinery Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fan Zhang, Guifa Teng, Jie Yao, and Sufen Dong

365

Research on Informationization Talented Person Training Pattern of the Countryside Area in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Wang and Zhongwei Sun

374

Research on Quality Index System of Digital Aerial Photography Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wencong Jiang, Yanling Li, Yong Liang, and Yanwei Zeng

381


XIX

Research on Quality Inspection Method of Digital Aerial Photography Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaojun Wang, Yanling Li, Yong Liang, and Yanwei Zeng

392

On RFID Application in the Tracking and Tracing System of Agricultural Product Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weihua Gan, Yuwei Zhu, and Tingting Zhang

400

Research on Rough Set and Decision Tree Method Application in Evaluation of Soil Fertility Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guifen Chen and Li Ma

408

Research on the Method of Geospatial Information Intelligent Search Based on Search Intention Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingbo Liu, Jian Wang, and Bingbo Gao

415

Research on the Theory and Methods for Similarity Calculation of Rough Formal Concept in Missing-Value Context . . . . . . . . . . . . . . . . . . . . Wang Kai, Li Shao-Wen, Zhang You-Hua, and Liu Chao

425

Research on Traceability System of Food Safety Based on PDF417 Two-Dimensional Bar Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shipu Xu, Muhua Liu, Jingyin Zhao, Tao Yuan, and Yunsheng Wang

434

Research and Application of Cultivation-Simulation- Optimization Decision Making System for Rapeseed (Brassica napus L.) . . . . . . . . . . . . Hongxin Cao, Chunlei Zhang, Baojun Zhang, Suolao Zhao, Daokuo Ge, Baoqing Wang, Chuanbao Zhu, David B. Hannaway, Dawei Zhu, Juanuan Zhu, Jinying Sun, Yan Liu, Yongxia Liu, and Xiufang Wei Residue Dynamics of Phoxim in Pericarp, Sarcocarp and Kernel of Apple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yunxia Luan, Hua Ping, and Ligang Pan Risk Analysis of Aedes triseriatus in China . . . . . . . . . . . . . . . . . . . . . . . . . . Jingyuan Liu, Xiaoguang Ma, Zhihong Li, Xiaoying Wu, and Nan Sun Risk Assessment of Reclaimed Water Utilization in Basin Based on GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanxia Zheng, Shaoyuan Feng, Na Jiang, and Qingyi Meng

441

457 465

473

Root Architecture Modeling and Visualization in Wheat . . . . . . . . . . . . . . Liang Tang, Feng Tan, Haiyan Jiang, Xiaojun Lei, Weixing Cao, and Yan Zhu

479

Sensors in Smart Phone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunmei Pei, Huiling Guo, Xiuqing Yang, Yangqiu Wang, Xiaojing Zhang, and Hairong Ye

491

XX


Simulation Analyze the Dice and Shape of the Dicer Based on ADAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingsa Huang, Jianping Hu, Deyong Yang, Xiuping Shao, and Fa Liu

496

Simulation and Design of Mixing Mechanism in Fertilizer Automated Proportioning Equipment Based on Pro/E and CFD . . . . . . . . . . . . . . . . . Liming Chen and Liming Xu

505

Simulation Study of a Novel Algorithm for Digital Relaying Based on FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Renwang He, Dandan Xie, Yuling Zhao, and Yibo Yang

517

Simulation Study of Single Line-to-Ground Faults on Rural Teed Distribution Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wanying Qiu

521

Single Leaf Area Measurement Using Digital Camera Image . . . . . . . . . . . Baisong Chen, Zhuo Fu, Yuchun Pan, Jihua Wang, and Zhixuan Zeng

525

Sliding Monitoring System for Ground Wheel Based on ATMEGA16 for No-Tillage Planter—CT246 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lianming Xia, Xiangyou Wang, Duayang Geng, and Qingfeng Zhang

531

Soil Erosion Features by Land Use and Land Cover in Hilly Agricultural Watersheds in Central Sichuan Province, China . . . . . . . . . . . . . . . . . . . . . . Zhongdong Yin, Changqing Zuo, and Liang Ma

538

Spatial and Temporal Variability of Annual Precipitation during 1958–2007 in Loess Plateau, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rui Guo, Fengmin Li, Wenying He, Sen Yang, and Guojun Sun

551

Spatial Statistical Analysis in Cow Disease Monitoring Based on GIS . . . Lin Li, Yong Yang, Hongbin Wang, Jing Dong, Yujun Zhao, and Jianbin He

561

Study for Organic Soybean Production Information Traceability System Based on Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xi Wang, Chun Wang, Xinzhong Wang, and Weidong Zhuang

567

Study of Agricultural Informatization Standards Framework . . . . . . . . . . . Yunpeng Cui, Shihong Liu, and Pengju He

573

On Countermeasures of Promoting Agricultural Products’ E–Commerce in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weihua Gan, Tingting Zhang, and Yuwei Zhu

579

Study on Approaches of Land Suitability Evaluation for Crop Production Using GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linyi Li, Jingyin Zhao, and Tao Yuan

587


XXI

Tracking of Human Arm Based on MEMS Sensors . . . . . . . . . . . . . . . . . . . Yuxiang Zhang, Liuyi Ma, Tongda Zhang, and Fuhou Xu

597

Study on Integration of Measurement and Control System for Combine Harvester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Chen, Yuelan Zheng, Yaoming Li, and Xinhua Wei

607

Study on Jabber Be Applied to Video Diagnosis for Plant Diseases and Insect Pests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Zhang, JunFeng Zhang, Feng Yu, JiChun Zhao, and RuPeng Luan

615

Study on Pretreatment Algorithm of Near Infrared Spectroscopy . . . . . . . Xiaoli Wang and Guomin Zhou

623

Study on Rapid Identification Methods of Transgenic Rapeseed Oil Based on Near Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shiping Zhu, Jing Liang, and Lin Yan

633

Study on Regional Agro-ecological Risk and Pressure Supported by City Expansion Model and SERA Model – A Case Study of Selangor, Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxia Shi, Yaoli Zhang, and Cheng Peng Study on Relationship between Tobacco Canopy Spectra and LAI . . . . . . Hongbo Qiao, Weng Mei, Yafei Yang, Wang Yong, Jishuai Zhang, and Yu Hua Study on Spatial Scale Transformation Method of MODIS NDVI and NOAA NDVI in Inner Mongolia Grassland . . . . . . . . . . . . . . . . . . . . . . . . . . Hongbin Zhang, Guixia Yang, Qing Huang, Gang Li, Baorui Chen, and Xiaoping Xin Study on Storage Characteristic of Navel Orange Based on ANN . . . . . . . Junfang Xia and Runwen Hu Study on the Differences of Village-Level Spatial Variability of Agricultural Soil Available K in the Typical Black Soil Regions of Northeast China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weiwei Cui and Jiping Liu Study on the Management System of Farmland Intelligent Irrigation . . . Fanghua Li, Bai Wang, Yan Huang, Yun Teng, and Tijiu Cai Extracting Winter Wheat Planting Area Based on Cropping System with Remote Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xueyan Sui, Xiaodong Zhang, Shaokun Li, Zhenlin Zhu, Bo Ming, and Xiaoqing Sun

641 650

658

667

674 682

691

XXII


Study on the Rainfall Interpolation Algorithm of Distributed Hydrological Model Based on RS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxia Yang, Yong Liang, and Song Jia Study on Vegetable Field Evaluation Index System for Non-Point Source Pollution of Dagu River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinheng Zhang, Junqiang Wang, Yongliang Lv, Jianting Liu, Dapeng Li, Zhenxuan Yao, Xi Jiang, and Ying Liu

700

706

Study on Water Resources Optimal Allocation of Irrigation District and Irrigation Decision Support System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liang Zhang, Daoxi Li, and Xiaoyu An

716

Study on Web-Based Cotton Fertilization Recommendation and Information Management Decision Support System . . . . . . . . . . . . . . . . . . . Yv-mei Dang and Xin Lv

726

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

735

Table of Contents – Part III

Study on XML-Based Heterogeneous Agriculture Database Sharing Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiulan Wu, Yongxiang Sun, Xiaoxia Yang, Yong Liang, and Xia Geng

1

Studying on Construction Programs of the Platform of Primary Products Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gang Lu, Peng Lu, and Cuie Liu

8

Supply Chain Integration Based on Core Manufacturing Enterprise . . . . . Wenqin Cao and Haiyan Zhu

14

Target Recognition for the Automatically Targeting Variable Rate Sprayer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maogang Li, Yan Shi, Xingxing Wang, and Haibo Yuan

20

Target Recognition of Software Research about Machine System of Accurately Spraying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Shi, Chunmei Zhang, Maogang Li, and Haibo Yuan

29

The Application of CPLD and ARM in Food Safety Testing Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianjun Ding, Xihua Wang, and Chao Sun

36

The Application of Three-Dimensional Visualization Technology in Village Information Service Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxia Yang, Yong Liang, and Song Jia

41

Research and Application of Data Security for Mobile Devices . . . . . . . . . Xiandi Zhang, Feng Yang, Zhongqiang Liu, Zhenzhi Wang, and Kaiyi Wang The Design and Development of the Land Management System in Dingzhuang Town Based on Spatial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . Yusheng Liang, Wenbin Sun, Haiting Diao, and Ying Li The Design of Portable Equipment for Greenhouse’s Environment Information Acquirement Based on Voice Service . . . . . . . . . . . . . . . . . . . . Xin Zhang, Xiaojun Qiao, Wengang Zheng, Cheng Wang, and Yunhe Zhang The Design of Smart Wireless Carbon Dioxide Measuring Instrument Used in Greenhouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wengang Zheng, Xin Zhang, Xiaojun Qiao, Hua Yan, and Wenbiao Wu

46

57

66

75

XXIV


The Detection of Quality Deterioration of Apple Juice by Near Infrared and Fluorescence Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dazhou Zhu, Baoping Ji, Zhaoshen Qing, Cheng Wang, and Manuela Zude The Determination of Total N, Total P, Cu and Zn in Chicken Manure Using Near Infrared Reflectance Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . Yiwei Dong, Yongxing Chen, Dazhou Zhu, Yuzhong Li, Chunying Xu, Wei Bai, Yanan Wang, and Qiaozhen Li The Growth Phases of Information Construction in Chinese Rural Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li-jun Wang The Judgment of Beef Marble Texture Based on the MATLAB Image Processing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruokui Chang, Yong Wei, Lizhen Ma, Yuanhong Wang, Hua Liu, and Mingyu Song

84

92

99

106

The New Method of Fruit Tree Characteristics Acquisition Using Electromagnetic Tracking Instrument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jian Wang, Ding-Feng Wu, Guo-Min Zhou, and Yun Qiu

113

The Novel Integrating Sphere Type Near-Infrared Moisture Determination Instrument Based on LabVIEW . . . . . . . . . . . . . . . . . . . . . . Yunliang Song, Bin Chen, Shushan Wang, Daoli Lu, and Min Yang

123

The Research and Realization of the Science Feed Management System in Islamic Livestock Norm Production and Quality Attestation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rong Ren and Wenxing Bao

132

The Simulation of the Apple Tree Form’s Effects on Its Photosynthetic Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lin Hu, Guomin Zhou, and Yun Qiu

138

The Spatial and Temporal Prognosis of Oilseed Yield in Shandong Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yujian Yang, Jianhua Zhu, Shubo Wan, and Xiaoyan Zhang

146

The Study and Implementation of Agricultural Information Service System Based on Addressable Broadcast . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huoguo Zheng, Haiyan Hu, Shihong Liu, and Hong Meng

158

The Study of Quality and Safety Traceability System of Vegetable Produce of Hebei Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fangzhou Wang and Wensheng Sun

165


XXV

The Study on Building of Virtual Reality System in Large Surface Coal Mine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baoying Ye, Nisha Bao, and Zhongke Bai

173

The Study on Navel Orange Traceability Chain . . . . . . . . . . . . . . . . . . . . . . Huoguo Zheng, Xianxue Meng, and Shihong Liu

179

The Study on the Organization Approach of Agricultural Model Components Library Based on Topic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . Haiyan Jiang, Bing Fu, Mei Zhang, Yan Zhu, and Weixin Cao

186

Theory of Double Sampling Applied to Main Crops Acreage Monitoring at National Scale Based on 3S in China—CT316 . . . . . . . . . . . . . . . . . . . . . Quan Wu, Li Sun, Fei Wang, and Shaorong Jia

198

Three-Dimensional Visualization of Soil Electrical Conductivity Variation by VRML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongyi Li

212

Towards Developing an Edible Fungi Factory HACCP MIS Base on RFID Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yunsheng Wang, Shipu Xu, ChangZhao Wan, Jihong Cheng, Qian Guo, Juan Yang, and Jingying Zhao

222

Toxicity of Cu, Pb, and Zn on Seed Germination and Young Seedlings of Wheat (Triticum Aestivum L.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haiou Wang, Guangrong Zhong, Guoqing Shi, and Fangting Pan

231

Using Data Grid Technology to Build MODIS Data Management System in Agriculture Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi Zeng and Guoqing Li

241

Virtual Prototype Modeling and Simulating Analysis of Lotus Root Slicing Machine Based on ADAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianping Hu, Jing Wang, Yinsa Huang, and Enzhu Wei

249

Virtual Reality and the Application in Virtual Experiment for Agricultural Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Zang, Zhongxiang Zhu, Zhenghe Song, and Enrong Mao

257

Virtual Visualization System for Growth of Tobacco Root . . . . . . . . . . . . . Lei Xi, Shuping Xiong, Yanna Ren, Qiang Wang, Juan Yang, Longlong Zhang, and Xinming Ma Winter Wheat Quality Inspection and Regionalization Based on NIR Network and Remote Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaodong Yang, WenJiang Huang, Cunjun Li, Xingang Xu, and Hao Yang

269

280

XXVI


A Web-Based Monitoring System as a Measurement Tool in Greenhouses Using Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . Yuling Shi, Zhongyi Wang, Xu Liu, Dongjie Zhao, and Lan Huang

289

Analysis and Design on Decision Support System of Security Risk Management in Rural Power Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dongsheng Zhou and Tao Yang

298

Analysis on the Factors Causing the Real-Time Image Blurry and Development of Methods for the Image Restoration . . . . . . . . . . . . . . . . . . Jianhua Zhang, Ronghua Ji, Kaiqun Hu, Xue Yuan, Hui Li, and Lijun Qi

304

Application Analysis of Machine Vision Technology in the Agricultural Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Yang, Yang Zhang, and Tian He

316

Comparative Study of Methods of Risks Assessment in Rural Power Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoqiang Song and Tao Yang

322

A Circuit Module and CPLD Laser Ground Controller Based on RS485 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinlei Li, Gang Liu, Mingming Guo, Yin Liu, and Fei Yang

327

Design of Decision Support System for Mechanical Conservation Tillage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junjing Yuan, Jian Zhang, and Hongzhen Cai

341

Experimental Study on the Quality of Dutch Cucumber in Storage . . . . . Jingying Tan, Dan Jin, and Qing Wang

347

GIS-Based Evaluation of Soybean Growing Areas Suitability in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenying He, Sen Yang, Rui Guo, Yaxiong Chen, Weihong Zhou, Chaojie Jia, and Guojun Sun

357

Goal-Driven Workflow Generation Based on AI Planning . . . . . . . . . . . . . . Yinxue Shi, Minghao Yang, and Ruizhi Sun

367

Mobile Phones of 3G Era in Small and Medium-Sized Agricultural Production and Application Prospect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Yang, Tian He, and Yang Zhang

375

Research of Dynamic Identification Technology on Cotton Foreign Fibers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuangxi Liu, Wenxiu Zheng, Hengbin Li, and Jinxing Wang

379


Research on Acquisition Methods of High-Precision DEM for Distributed Hydrological Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li Deng, Yong Liang, and Chengming Zhang Research on Image Classification Algorithm Based on Artificial Immune Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chengming Zhang, Yong Liang, ShuJing Wan, Jinping Sun, and Dalei Zhang

XXVII

390

403

Short-Term Load Forecasting Based on RS-ART . . . . . . . . . . . . . . . . . . . . . Tao Yang, Feng Zhang, Qingji Li, and Ping Yang

413

Study on Delineation of Irrigation Management Zones Based on Management Zone Analyst Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiuxiang Jiang, Qiang Fu, and Zilong Wang

419

Study on Irrigation Regime of Double Cropping of Winter Wheat with Summer Maize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shengfeng Wang, Jianxin Xu, Shuqin Yang, and Ping Jia

428

Study on Model of Risk Assessment of Standard Operation in Rural Power Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qingji Li and Tao Yang

440

Study on Refrigeratory Compressor with Frequency Conversion and Its Economical Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dan Jin, Jingying Tan, and Qing Wang

445

Study on the Parameters’ Acquisition Method of Distributed Hydrological Model Based on RS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinping Sun, Yong Liang, Qin Yan, and Chengming Zhang

452

The Design and Implementation of Halal Beef Wholly Quality Traceability System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yongsheng Yang and Wenxing Bao

464

The Development of Remote Labor Training System for Rural Small Towns Based on MVC Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lihua Zheng, Dongmei Zhao, Nan Zhou, Xiaobing Qiu, Li Xu, Shicong Wang, and Zhong Qiao WEB-Based Intelligent Diagnosis System for Cotton Diseases Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui Li, Ronghua Ji, Jianhua Zhang, Xue Yuan, Kaiqun Hu, and Lijun Qi Wetland Information Extraction from RS Image Based on Wavelet Packet and the Active Learning Support Vector Machine . . . . . . . . . . . . . . Pu Wang and Wenxing Bao

473

483

491

XXVIII


A Research to Construct the Interactive Platform for Integrated Information of Agricultural Products in China Xinjiang . . . . . . . . . . . . . . . Yuan Li and Zhigang Li Modeling Spatial Pattern of Precipitation with GIS and Multivariate Geostatistical Methods in Chongqing Tobacco Planting Region, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuan Wang, Jiake Lv, Chaofu Wei, and Deti Xie

500

512

Prediction of Freight Ability in Country Base on GRNN . . . . . . . . . . . . . . Baihua Zhang

525

Research and Application of Modern Information Technology in the Forest Plant Protection Machinery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lairong Chen, Qingchun Wang, and Ronghua Ji

532

Research on Information Sharing Pattern of Agricultural Products Supply Chain Based on E-Commerce Technology . . . . . . . . . . . . . . . . . . . . Zhigang Li, Yang Gao, Yuan Li, and Jinyu Han

539

Study of Intelligent Integrated Modeling and Development of Agricultural Post-Project Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen Li

549

Study of Optimal Operation for Huai’an Parallel Pumping Stations with Adjustable-Blade Units Based on Two Stages Decomposition-Dynamic Programming Aggregation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi Gong, Jilin Cheng, Rentian Zhang, and Lihua Zhang TBIS: A Web-Based Expert System for Identification of Tephritid Fruit Flies in China Based on DNA Barcode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhimei Li, Zhihong Li, Fuxiang Wang, Wei Lin, and Jiajiao Wu TPPADS: An Expert System Based on Multi-branch Structure for Tianjin Planting Pest Assistant Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhigang Wu, Yichuan Bai, Han Huang, Wenxin Li, Zhimei Li, and Zhihong Li

554

563

572

Study on the Demands for Agricultural and Rural Informationization in China and Its Strategic Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Li, Chunjiang Zhao, Xiangyang Qin, and Gang Liu

580

Study on the Near Infrared Model Development of Mixed Liquid Samples by the Algorithm of OSC-PLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dong Wang, Zhihong Ma, Shengfeng Ye, and Shungeng Min

592

Design and Realization of Information Service System of Agricultural Expert Based on Wireless Mobile Communication Technology . . . . . . . . . Jianshe Zhao, Wenyue Li, Yong Yang, Haili Meng, and Wen Huang

598


XXIX

Design of a New Soil-Tuber Separation Device on Potato Harvesters . . . . Gaili Gao, Dongxing Zhang, and Jun Liu

604

Fast Discrimination of Nanfeng Mandarin Varieties Based on Near Infrared Spectroscopy Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huamao Zhou, Chao Zhou, Honghui Rao, and Yande Liu

613

Purity Identification of Maize Seed Based on Color Characteristics . . . . . Xiaomei Yan, Jinxing Wang, Shuangxi Liu, and Chunqing Zhang

620

Reconstructing Vegetation Temperature Condition Index Based on the Savitzky–Golay Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manman Li and Junming Liu

629

Research and Implementation of Agricultural Science and Technology Consulting System Based on Ajax and Improved VSM . . . . . . . . . . . . . . . . Sufen Sun, Junfeng Zhang, Changshou Luo, and Qingfeng Wei

638

Empirical Study on the Relationship between ICT Application and China Agriculture Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pengju He, Shihong Liu, Huoguo Zheng, and Yunpeng Cui

648

The Research of the Agricultural Technology Transfer . . . . . . . . . . . . . . . . JinYou Hu, Jingjing Zhang, and Jian Zhang

656

Research on the Collaboration Service Mechanism for Pig Diseases Diagnosis Based on Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiang Sun, Huarui Wu, Huaji Zhu, Cheng Peng, and Jingqiu Gu

661

Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changshou Luo, Qingfeng Wei, Liying Zhou, Junfeng Zhang, and Sufen Sun Research on the Application Integration Model for the Agricultural Enterprise of Integrative Production and Marketing . . . . . . . . . . . . . . . . . . Feng Yang, Xiandi Zhang, Zhongqiang Liu, Zhenzhi Wang, and Kaiyi Wang A SaaS-Based Logistics Informatization Model for Specialized Farmers Cooperatives in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhongqiang Liu, Kaiyi Wang, Shufeng Wang, Feng Yang, and Xiandi Zhang

672

682

696

Study on Acoustic Features of Laying Hens’ Vocalization . . . . . . . . . . . . . . Ligen Yu, Guanghui Teng, Zhizhong Li, and Xuming Liu

704

A Study on Pig Slaughter Traceability Solution Based on RFID . . . . . . . . Qingyao Luo, Benhai Xiong, Zhi Geng, Liang Yang, and Jiayi Pan

710

XXX


Study on Application of Location Algorithm Base Multidimensional Spatial Information in the Situation Analysis of Natural Ecology . . . . . . . Jumei Ai and Shuhua Mao

721

A Water-Quality Dynamic Monitoring System Based on Web-Server-Embedded Technology for Aquaculture . . . . . . . . . . . . . . . . . . . Dongxian He, Daoliang Li, Jie Bao, Hu Juanxiu, and Shaokun Lu

725

A Study on Operation Strategies of Unclogging Container-Trailers Enterprises at Shenzhen Port . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoliang Gao and Nie Dan

732

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

741

Table of Contents – Part IV

A Compression Method of Decision Table Based on Matrix Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laipeng Luo and Ergen Liu

1

A Laplacian of Gaussian-Based Approach for Spot Detection in Two-Dimensional Gel Electrophoresis Images . . . . . . . . . . . . . . . . . . . . . . . . Feng He, Bangshu Xiong, Chengli Sun, and Xiaobin Xia

8

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chun-Hua Xiao, Shao-Kun Li, Ke-Ru Wang, Yan-Li Lu, Jun-Hua Bai, Rui-Zhi Xie, Shi-Ju Gao, Qiong Wang, and Fang-Yong Wang

16

A New Color Information Entropy Retrieval Method for Pathological Cell Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiangang Jiang, Qing Liang, and Tao Shen

30

A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . Jia wei Li and Ming Sun

39

A Non-linear Model of Nondestructive Estimation of Anthocyanin Content in Grapevine Leaves with Visible/Red-Infrared Hyperspectral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . JiangLin Qin, Donald Rundquist, Anatoly Gitelson, Zongkun Tan, and Mark Steele

47

Application of Improved BP Neural Network in Controlling the Constant-Force Grinding Feed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhaoxia Chen, Bailin He, and Xianfeng Xu

63

A Semantic Middleware of Grain Storage Internet . . . . . . . . . . . . . . . . . . . . Siquan Hu, Haiou Wang, Chundong She, and Junfeng Wang

71

AE Feature Analysis on Welding Crack Defects of HG70 Steel Used by Truck Crane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yantao Dou, Xiaoli Xu, Wei Wang, and Siqin Pang

78

An Equilateral Triangle Waveguide Beam Splitter . . . . . . . . . . . . . . . . . . . . Zhimin Liu, Fengqi Zhou, Hongjian Li, Bin Tang, Zhengfang Liu, Qingping Wu, Aixi Chen, and Kelin Huang

89

Analysis and Implementation of Embedded SNMP Agent . . . . . . . . . . . . . Hubin Deng, Guiyuan Liu, and Lei Zhang

96

XXXII


Application of Computer Technology in Advanced Material Science and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yajuan Liu

103

Application of Interferometry in Ultrasonic System for Vibration . . . . . . . Zhengping Liu, Shenghang Xu, and Juanjuan Liu

108

Automatic Control System for Highway Tunnel Lighting . . . . . . . . . . . . . . Shijuan Fan, Chao Yang, and Zhiwei Wang

116

Comparative Study of Distance Discriminant Analysis and Bp Neural Network for Identification of Rapeseed Cultivars Using Visible/Near Infrared Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Zou, Hui Fang, Fei Liu, Wenwen Kong, and Yong He Current Situation and Prospect of Grassland Management Decision Support Systems in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qingwei Duan, Xiaoping Xin, Guixia Yang, Baorui Chen, Hongbin Zhang, Yuchun Yan, Xu Wang, Baohui Zhang, and Gang Li

124

134

Design Method and Implementation of Ternary Logic Optical Calculator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunzhi Li and Junyong Yan

147

Design of Automatic Cutting and Welding Machine for Brake Beam-Axle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leping Liu, Mingdong Zhong, and Qizheng Dong

167

Design of Multifunction Vehicle Bus Controller . . . . . . . . . . . . . . . . . . . . . . Zhongqi Li, Fengping Yang, and Qirong Xing

177

Detection of Soil Total Nitrogen by Vis-SWNIR Spectroscopy . . . . . . . . . Yaoze Feng, Xiaoyu Li, Wei Wang, and Changju Liu

184

Development and Application of Tennis Match Video Retrieval Technology in Multimedia Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shehua Cao

192

Fault Diagnosis of Roller Bearing Based on PCA and Multi-class Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guifeng Jia, Shengfa Yuan, and Chengwen Tang

198

Health Status Identification of Connecting Rod Bearing Based on Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yongbin Liu, Qingbo He, Ping Zhang, Zhongkui Zhu, and Fanrang Kong Investigation of the Methods for Tool Wear On-Line Monitoring during the Cutting Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongjiang Chen

206

215


XXXIII

Magnetic-Field-Based 3D ETREE Modelling for Multi-Frequency Eddy Current Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Zhang and Yong Li

221

Measurement of Self-emitting Magnetic Signals from a Precut Notch of Q235 Steel during Tensile Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lihong Dong, Binshi Xu, and Shiyun Dong

231

Modeling and Performance Analysis of Giant Magnetostrictive Microgripper with Flexure Hinge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qinghua Cao, Quanguo Lu, Junmei Xi, Jianwu Yan, and Changbao Chu Non-destructive Measurement of Sugar Content in Chestnuts Using Near-Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jie Liu, Xiaoyu Li, Peiwu Li, Wei Wang, Jun Zhang, Wei Zhou, and Zhu Zhou

237

246

Nondestructive Testing Technology and Optimization of On-Service Urea Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoling Luo and Lei Deng

255

Parameters Turning of the Active-Disturbance Rejection Controller Based on RBF Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baifen Liu and Ying Gao

260

Research of Intelligent Gas Detecting System for Coal Mine . . . . . . . . . . . Hui Chen

268

Research of Subway’s Train Control System Based on TCN . . . . . . . . . . . Qingfeng Ding, Fengping Yang, and Qixin Zhu

279

Shape Detection for Impeller Blades by Non-contact Coordinate Measuring Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shimin Luo

286

Simulation of Road Surface Roughness Based on the Piecewise Fractal Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhixiong Lu, Lanying Zhao, Xiaoqin Li, and Jun Yuan

294

Stress Analysis near the Welding Interface Edges of a QFP Structure under Thermal Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhigang Huang, Xuecheng Ping, and Pingan Liu

306

Study of Intelligent Diagnosis System for Mechanism Wear Fault Based on Fuzzy-Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanmao Xie

314

XXXIV


Study on Autonomous Path Planning by Mobile Robot for Road Nondestructive Testing Based on GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lunhui Xu, Fan Ye, and Yanguo Huang

321

Study on Imitating Grinding of Two-Dimensional Ultrasonic Vibration Turning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leping Liu, Wen Zhao, and Yuan Ma

333

Study on Optimal Path Changing Tools in CNC Turret Typing Machine Based on Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Liu, XiaoLing Ding, YinFa Yan, and Xin Ci

345

Study on the Problem and Countermeasure of Fruit Production Quality and Safety in Yanshan Mountain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haisheng Gao, Bin Du, and Fengmei Zhu

355

Calculation and Analysis of Double-Axis Elliptical-Parabolic Compond Flexure Hinge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ping’ an Liu, Jianqun Cheng, and Zhigang Lai

361

Surface Distresses Detection of Pavement Based on Digital Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aiguo Ouyang, Chagen Luo, and Chao Zhou

368

The Application Research of Neural Network in Embedded Intelligent Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaodong Liu, Dongzhou Ning, Hubin Deng, and Jinhua Wang

376

The Theoretical Analysis of Test Result’s Errors for the Roller Type Automobile Brake Tester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Li, Xiaojing Zha, and Dongsheng Wu

382

A Type of Arithmetic Labels about Circulating Ring . . . . . . . . . . . . . . . . . Ergen Liu, Dan Wu, and Kewen Cai

390

Application of CPLD in Pulse Power for EDM . . . . . . . . . . . . . . . . . . . . . . . Yang Yang and Yanqing Zhao

398

Application of IDL and ENVI Redevelopment in Hyperspectral Image Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Long Xue

403

Design of Integrated Error Compensating System for the Portable Flexible CMMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qing-Song Cao, Jie Zhu, Zhi-Fan Gao, and Guo-Liang Xiong

410

Detecting and Analyzing System for the Vibration Comfort of Car Seats Based on LabVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Qiu

420


XXXV

Determination of Pesticide Residues on the Surface of Fruits Using Micro-Raman Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yande Liu and Tao Liu

427

Development of the Meter for Measuring Pork Quality Based on the Electrical Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhen Xing, Wengang Zheng, Changjun Shen, and Xin Zhang

435

Experimental Investigation of Influence on Non-destructive Testing by Form of Eddy Current Sensor Probe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengyun Xie and Jihui Zhou

443

Feasibility of Coordinate Measuring System Based on Wire Driven Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ji-Hui Zhou, Qing-Song Cao, Fa-Xiong Sun, and Lan Bi

450

HSFDONES: A Self-Leaning Ontology-Based Fault Diagnosis Expert System Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XiangBin Xu

460

Nondestructive Measurement of Sugar Content in Navel Orange Based on Vis-NIR Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunsheng Luo, Long Xue, Muhua Liu, Jing Li, and Xiao Wang

467

Numerical Simulation of Temperature Field in Selective Laser Sintering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jian Zhang, Deying Li, Jianyun Li, and Longzhi Zhao

474

Numerical Simulations of Compression Properties of SiC/Al Co-continuous Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingjuan Zhao, Na Li, Longzhi Zhao, and Xiaolan Zhang

480

Simulation and Optimization in Production Logistics Based on eM-Plant Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XinJian Zhou, XiangBin Xu, and Wei Zhu

486

Simulation of Transient Temperature Field in the Selective Laser Sintering Process of W/Ni Powder Mixture . . . . . . . . . . . . . . . . . . . . . . . . . . Jiwen Ren, Jianshu Liu, and Jinju Yin

494

Study on Plant Nutrition Indicator Using Leaf Spectral Transmittance for Nitrogen Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juanxiu Hu, Dongxian He, and Po Yang

504

Study on the Influence of Non-electrical Parameters on Processing Quality of WEDM-HS and Improvement Measures . . . . . . . . . . . . . . . . . . . Guangyao Xiong, Meizhu Zheng, Deying Li, Longzhi Zhao, Yanlin Wang, and Minghui Li

514

XXXVI


Test Analysis and Theoretical Calculation on Braking Distance of Automobile with ABS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dongsheng Wu, Jun Li, Xiaoping Shu, Xiaojing Zha, and Beili Xu The Detection of Early-Maturing Pear’s Effective Acidity Based on Hyperspectral Imaging Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pengbo Miao, Long Xue, Muhua Liu, Jing Li, Xiao Wang, and Chunsheng Luo The Effects of Internal and External Factors on the Mechanical Behavior of the Foam Copper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Longzhi Zhao, Xiaolan Zhang, Na Li, Mingjuan Zhao, and Jian Zhang

521

528

537

Optimum Design of Runner System for Router Cover Based on Mold Flow Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tangqing Kuang and Wenjuan Gu

543

Design of Tread Flange Injection Mold Based on Pro/E . . . . . . . . . . . . . . . Huilan Zhou

555

Study on the Online Control System to Prevent Drunk Driving Based on Photoelectric Detection Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lu Liming, Yang Yuchuan, and Lu Jinfu

563

The Design and Simulation of Electro-Hydraulic Velocity Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengtao Lin

568

Application of Background Information Database in Trend Change of Agricultural Land Area of Guangxi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Yang, Shiquan Zhong, Yuhong Li, Weiping Lu, and Chaohui Wu

575

Reasons of the Incremental Information in the Updating Spatial Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaji Zhu, Huarui Wu, and Xiang Sun

583

Research on Non-point Source Pollution Based on Spatial Information Technology: A Case Study in Qingdao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Shen, Jinheng Zhang, and Junqiang Wang

592

The Regulation Analysis of Low-Carbon Orientation for China Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bikai Gong and Bing Chen

602

A CDMA-Based Soil-Quality Monitoring System for Mineland Reclamation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dongxian He, Daoliang Li, Jie Bao, and Shaokun Lu

610


XXXVII

Design and Implementation of a Low-Power ZigBee Wireless Temperature Humidity Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuipeng Gong, Changli Zhang, Lili Ma, Junlong Fang, and Shuwen Wang Land Evaluation Supported by MDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengchang Xue Design and Development of Water Quality Monitoring System Based on Wireless Sensor Network in Aquaculture . . . . . . . . . . . . . . . . . . . . . . . . . Mingfei Zhang, Daoliang Li, Lianzhi Wang, Daokun Ma, and Qisheng Ding

616

623

629

Design of an Intelligent PH Sensor for Aquaculture Industry . . . . . . . . . . . Haijiang Tai, Qisheng Ding, Daoliang Li, and Yaoguang Wei

642

A Simple Temperature Compensation Method for Turbidity Sensor . . . . . Haijiang Tai, Daoliang Li, Yaoguang Wei, Daokun Ma, and Qisheng Ding

650

A Wireless Intelligent Valve Controller for Agriculture Integrated Irrigation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nannan Wen, Daoliang Li, Daokun Ma, and Qisheng Ding

659

Evaluation of the Rural Informatization Level in Central China Based on Catastrophe Progression Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lingxian Zhang, Xue Liu, Zetian Fu, and Daoliang Li

672

GIS-Based Evaluation on the Eco-Demonstration Construction in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lingxian Zhang, Juncheng Ma, Daoliang Li, and Zetian Fu

680

Modeling and Analysis of Pollution-Free Agricultural Regulatory Based on Petri-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fang Wang, Qingling Duan, Lingzi Zhang, and Guo Li

691

An Online Image Segmentation Method for Foreign Fiber Detection in Lint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daohong Kan, Daoliang Li, Wenzhu Yang, and Xin Zhang

701

An Efficient Iterative Thresholding Algorithms for Color Images of Cotton Foreign Fibers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Zhang, Daoliang Li, Wenzhu Yang, Jinxing Wang, and Shuangxi Liu Application of Grey Prediction Model in Rural Informatization Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Du, Daoliang Li, Hongwen Li, and Lifeng Shen

710

720

XXXVIII


Study on Evaluation Method for Chinese Agricultural Informatization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoqing Yuan, Liyong Liu, and Daoliang Li

727

Research on Calculation Method for Agricultural Informatization Contribution Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liyong Liu, Qilong Pan, and Daoliang Li

735

An Empirical Research on the Evaluation Index Regarding the Service Quality of Agricultural Information Websites in China . . . . . . . . . . . . . . . . Liyong Liu, Xiaoqing Yuan, and Daoliang Li

742

Hyperspectral Sensing Techniques Applied to Bio-masses Characterization: The Olive Husk Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giuseppe Bonifazi and Silvia Serranti

751

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

765

3-D Turbulence Numerical Simulation for the Flow Field of Suction Cylinder-Seeder with Socket-Slots* Yanjun Zuo1, Xu Ma1,2,**, Long Qi1, and Xinglong Liao1 1

College of Engineering, South China Agricultural University, Guangzhou, P.R. China 2 Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, P.R. China [email protected], [email protected], [email protected], [email protected]

Abstract. The flow field has significantly impact on seeding performance in the suction seeding device. A three-dimensional, incompressible, viscous, RNG turbulence model and the SIMPLE method were used by computational fluid dynamics(CFD), and the flow fields of suction cylinder-seeder with different socket’s radiuses were simulated by Fluent. When vacuum is 4kPa and productivity is 350 trays/h, the simulant results show that pressure is uniform, velocity is stable, energy loss mainly occurs near slots and outlet, and there is less interaction among socket-slots; The effect of flow field on socket’s radius to the cylinder isn’t significant by contrasting different socket’s radiuses on the average turbulent kinetic energy, the average vacuum and the maximum difference of velocity behind slots; The experimental results show that the best seeding performance is 84.73% when the socket’s radius is 5.5mm. Although the performance should be improved, but any sockets are never plugged, which shows enough that the seeder is a very promising precision seeding device. Keywords: Socket-slot, Suction cylinder, Flow field, Numerical simulation.

1 Introduction Usually, the demand of high-precision seeding is 2±1 seeds/bowl for super hybrid rice tray nursing seedlings, and can’t be satisfied invariably by the traditional mechanical seeding device. The suction seeding device is becoming mainstream for super hybrid rice with its advantage of low broken-seed rate, high single-seed rate, good generality, imprecise demand of seminal dimension and so on[1-2]. Flow field impacts seeding performance significantly in the suction seeding device, so it has been studied by many researchers at home and aboard. In overseas, Karayel D etc. have built mathematical model of vacuum pressure on a precision seeder[3], Guarella P etc. have studied the performance of a vacuum seeder nozzle for vegetable seeds in experiment and *

The paper is supported by the National Natural Science Fund Projects (Project number is 50775078), the National “11th Five-Year Plan” to support Projects (Project number is 2006BAD28B01-3), the earmarked fund for Modern Agro-industry Technology Research System and the fund for indraught of person with ability in colleges of Guangdong. ** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 1–8, 2011. © IFIP International Federation for Information Processing 2011

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theory[4]. In domestic, Yuan Yueming, Wang Zhaohui etc. have simulated and experimented the flow field for suction seeders of vertical disc and cylinder[5-6], Li Yaoming etc. have analyzed the flow field of sucking nozzle to suction seeder[7]. Suckers of existing suction seeding devices are usually plugged during seeding because rice is seeded with sprout. Although two-layer suction cylinder-seeder is developed by Pang Changle[8] and could relieve the suckers plugging, it can not satisfied the requirement of continuous seeding. So a seeding device will be developed with a new theory and new structure to solve this problem. Based on the traditional suction cylinders, a suction cylinder-seeder with socket-slots is developed which can solve the problem of suckers plugging effectively through seed-filling, seed-sucking, seedclearing and forcible sucker-clearing. In order to improve the seeding performance of suction cylinder-seeder with socket-slots, the numerical simulations for different socket’s radiuses have been calculated by software Computational Fluid Dynamics (CFD) in this paper. Socket’s radius was optimized by analyzing the distributions of velocity and pressure inside the cylinder, and checked by experiment. This study provides theoretical and practical basis for the design of super hybrid rice seed-metering device for nursing seedling.

2 Principle of Suction Cylinder-Seeder The suction cylinder-seeder with socket-slots for experiment is shown in figure 1. After flowing out of the seed hopper, rice seeds fall onto vibrant board. Then the

Fig. 1. Principe diagram of suction cylinder-seeder with socket-slots 1 Seed-protecting belt, 2 Seed-clearing rolling brush, 3 Suction vibrator, 4 Vibrant board, 5 Seed hopper, 6 Frame, 7 Conveyor belt, 8 Tray, 9 Pressure chamber, 10 Seed-popping springs, 11 Rice seeds, 12 Cylinder

3-D Turbulence Numerical Simulation for the Flow Field of Suction Cylinder-Seeder

3

seeds worm downward along with vibrant board under vibration and enter seedsucking area where prickle, broken sprout and other impurities are removed through the sieve meshes. The seed-feeding is accomplished under the gravity and vacuum. When the sockets rotate to seed-clearing area, the redundant seeds are cleared firstly by seed-clearing rolling brush rotating at the same direction with cylinder, and the residual seeds in sockets rotate into seed-protecting belt continually. The vacuum will be cut off when the sockets rotate into seeding and socket- clearing area, the springs pop the seeds and clear the sockets forcibly. The seeds fall into the appointed bowl under the gravity and elasticity, so realizing the precision seeding.

3 Numerical Simulation 3.1 Physical Model Along the axis, fifteen rows of sockets(R=4.5mm, 5.0mm, 5.5mm) that corresponding to the tray of 15×25 bowls are made outside cylinder. Along the circumference,

Fig. 2. Structure of cylinder

Fig. 3. Computational model of cylinder

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fifteen ring grooves are also made at the corresponding place of the sockets inside cylinder. The sockets and ring grooves are intersecting and forming a slot of 4.0mm. The specific dimensions are shown in figure 2. Because there are larger differences in local sizes, the model uses structured and unstructured grids to mesh, as is shown in figure 3. 3.2 Flow Equations Rotation of cylinder, two different flow fields which are connected by socket-slots and the effect of air viscidity, so turbulent swirling flow is formed starting from inlet of socket-slots nearby the insides of cylinder and socket-slots, and contains laminar flow and swirly shear flow at the wall of cylinder, jet flow of slots, free flow in the pipeline, flow with separation and so on. In order to ensure the accuracy of numerical simulation, turbulence model of RNG k-ε is used in this paper. In the model flow state, spatial coordinates, rotation and swirling flow state in the average flow have been considered, turbulent viscosity has been modified. And the model supplied an analytical formula with low Reynolds number which is more accuracy than standard equation of k-ε to the flow of near wall[9]. Turbulent kinetic energy and turbulent dissipation rate were calculated as follows: ∂ ∂ ∂ ∂k ( ρk ) + ( ρku i ) = (α k μ e ) + Gk + G p − ρε ∂t ∂x j ∂x j ∂x j ∂ ∂ ∂ ∂ε ε ( ρε ) + ( ρεui ) = (α ε μ e ) + (C1ε Gk − C 2ε ρε ) ∂t ∂xi ∂x j ∂x j k

(1) (2)

Where: k is the turbulent kinetic energy in m2/s2; ε is the turbulent dissipation rate in m2/s3; Gk is the generation item for turbulent kinetic energy(k) caused by average velocity gradient; Gp is the generation item for turbulent kinetic energy(k) caused by buoyancy; and μe is the turbulent viscosity in Pa·s, and μe=ρCμk2/s. Model constants[10] are C1ε=1.42, C2ε=1.68, Cμ=0.0845, αk=αε=1.39. 3.3 Boundary Condition The fluid is normal temperature air under standard condition, and its density is 1.205kg/m3, viscosity is 1.83×10-5Pa·s, and temperature is 293K, so the inlet and standard pressures are all 101325 Pa. The pressure inlet and outlet are all subsonic speed, and the wall uses adiabatic and no-slip boundary conditions[11].

4 Simulation Results and Analyses Numerical simulations for suction cylinder-seeder with three socket’s radiuses (R=4.5mm, 5.0 mm, 5.5 mm) use the method of semi-implicit method for pressurelinked equations(SIMPLE) when outlet pressure is 97325Pa and productivity is 350 trays/h, and the results are shown in figure 4 and table 1.


5

R=4.5mm R=5.0mm R=5.5mm Map of pressure

Map of velocity

Fig. 4. Distribution map of pressure and velocity

From the map of pressure in the figure 4, it can be known that pressure distributes uniformly in the whole cylinder and changes greatly at the slots and the outlet. The main reason is that, air fluid can’t turn suddenly like the wall as the inertial force is dominating when section of pipeline changes suddenly, disengage phenomena of main flow area and wall is occurring, and then swirling area forms. Distributional adjustment of velocity in main flow area, rotation of fluid in the swirling area and exchange of fluid particle in the two areas, all these lose energy, so energy loss occurs near the slots and outlet. From the map of velocity in the figure 4, it can be known that velocity is stable inside the cylinder, air fluids flow through socket-slots and have less interaction in the axial and circumference. Table 1. Contrast of different socket’s radiuses

R(mm)

ka(m2/s2)

Va(kPa)

vmax(m/s)

4.5

1.7838

2.10

13.98

5.0

1.7892

2.04

11.47

5.5

2.0793

1.88

9.22

Note: ka is the average turbulent kinetic energy; Va is the average vacuum behind slots; and vmax is the maximum difference of velocity behind slots.

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The contrasts of different socket’s radiuses on average turbulent kinetic energy, average vacuum and maximum difference of velocity after slot are shown in table 1. It can be known that: average turbulent kinetic energy increases along with the increases of socket’s radius, because the bigger socket’s radius, the stronger turbulization, the more significant effect of flow on eddy current, and the less stable flow field; average vacuum decreases along with the increases of socket’s radius, because the bigger socket’s radius, the more friction loss when air fluid through socket-slots; maximum difference of velocity decreases along with the increases of socket’s radius, because the bigger socket’s radius, the longer distance of air fluid pass socket-slots, the smaller difference of velocity, and the more stable seed-sucking; the effect of flow field on socket’s radius isn’t significant. The amount of seed-feeding is affected by socket’s radius, so the seeding performance would be checked.

5 Conclusions 5.1 Experimental Material The experimental material is super hybrid rice of Peizataifeng. Its dimension is shown in table 2. Table 2. Dimensions of gemmative rice seed Length(mm)

Width(mm)

Thickness(mm)

9.23

3.45

2.42

5.2 Experimental Procedures 1. 2. 3. 4. 5.

Rice seeds are soaked and germinated. The suction vibrator provides a stable pressure of 0.2MPa through fan setting. Vacuum of 4kPa is got by vacuum pump setting. The productivity of 350 trays/h is obtained by converter adjusting. The seed-clearing rolling brush starts and rotates at the same direction with cylinder, and its speed of 50rpm. 6. The seeds are put into seed hopper and vibrator starts, the vibrant board begins vibrating. And then the seed hopper starts and begins feeding seeds to the cylinder. Finally, seeding is experimented after the running is stable. 5.3 Experimental Factor According to the result analyses which were simulated by Fluent, control variable method is used to study the effect of seeding performance on socket’s radius, and the results of numerical simulation were checked.


7

5.4 Experimental Plan and Results The experiment has been done on the test-bed for nursing seedling at College of Engineering, South China Agricultural University. The main index is qualified rate (bowls of 1~3 seeds/all bowls ×100%))to be examined. The results are shown in table 3. Table 3. Experimental results

R(mm)

Rq(%)

Rr(%)

Rc(%)

4.5

78.89

16.47

4.64

5.0

81.69

11.74

6.57

5.5

84.73

6.31

8.96

Note: Rq is the qualified rate, Rr is the reseeding rate, and Rc is the cavity rate. It can be known from table 3 that, when socket’s radius is smaller, the seeds get in the sockets uneasier and are removed easier by seed-clearing rolling brush and the cavity rate is higher; when socket’s radius is bigger, the amount of seeds enter the sockets is larger and the reseeding rate is higher; the rising extent of reseeding rate is less than the falling extent of cavity rate because of seeding-clearing rolling brush, so the qualified rate increases along with the increases of socket’s radius. The seeding performance of suction cylinder-seeder with socket-slots is the highest(84.73%) when the socket’s radius is 5.5mm.

6 Conclusions Through the simulation and experiment of different socket’s radiuses when the vacuum is 4kPa and the productivity is 350trays/h, the conclusions are obtained as follows: 1 The distribution of pressure is uniform, velocity is stable, energy loss occurs mainly near the slots and outlet, and air fluid has less interaction among the slots in the cylinder. 2 The effect of flow field on socket’s radius is not significant to the cylinder, and the seeding performance is checked. 3 The experimental results show that the best seeding performance of suction cylinder-seeder is 84.73% when the socket’s radius is 5.5mm. In fact, there are many factors affecting the seeding performance, such as vacuum, productivity and positional angle of seed-feeding. Each factor is not the optimum as preliminary study, so the capability is not high at present, and will be studied further. There isn’t any sockets that are plugged in the whole process of experiment, which

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shows that the suction cylinder-seeder with socket-slots is a very promising precision seeding device.

Acknowledgements The paper is supported by the National Natural Science Fund Projects (Project number is 50775078), the National “11th Five-Year Plan” to support Projects (Project number is 2006BAD28B01-3), the earmarked fund for Modern Agro-industry Technology Research System and the fund for indraught of person with ability in colleges of Guangdong.

References 1. Zhou, H., Ma, X., Yao, Y.: Research advances and prospects in the seeding technology and equipment for tray nursing seedlings of rice. Transactions of the CSAE 24(4), 301–306 (2008) (in Chinese) 2. Wu, M., Tang, C., Li, M., et al.: The present situation and countermeasures about seeding apparatus of paddy precision seeder. Chinese agricultural mechanization 3, 30–31 (2003) (in Chinese) 3. Karayel, D., Barut, Z.B., Ozmerzi, A.: Mathematical Modelling of Vacuum Pressure on a Precision Seeder. Biosystems Engineering 87(4), 437–444 (2004) 4. Guarella, P., Pellerano, A., Pascuzzi, S.: Experimental and Theoretical Performance of a Vacuum Seeder Nozzle for Vegetable Seeds. Journal of Agricultural Engineering Research (64), 29–36 (1996) 5. Yuan, Y., Ma, X., Jin, H., et al.: Study on vacuum chamber fluid field of air suction seedmetering device for rice bud-sowing. Transactions of the CSAM 36(6), 42–44 (2005) (in Chinese) 6. Wang, Z., Ma, X., Dong, R., et al.: Numerical simulation for air field of air-suction cylinder seeder. Journal of Jilin Agricultural University 31(6), 781–784 (2009) (in Chinese) 7. Chen, J., Li, Y., Wang, X., et al.: Finite element analysis for the sucking nozzle air field of air-suction seeder. Transactions of the CSAM 38(9), 59–62 (2007) (in Chinese) 8. Changle, P., Zhuomao, E., Su, C., et al.: Design and experimental study on air-suction towlayer cylinder rice seeder. Transactions of the CSAE 5(9), 52–55 (2000) 9. Wang, F.: Analysis of Computational Fluid Dynamics: Theory and Application of Software CFD, pp. 124–125. Tsinghua University Press, Beijing (2005) (in Chinese) 10. Wu, B., Yan, H., Zhang, J.: Study on 3-D turbulent numerical simulation and performance foreacast of slurry pump. China Mechanical Engineering 20(5), 585–589 (2009) (in Chinese) 11. Deng, D.: Fluid flow handbook, p. 471. China Petrochemical Press, Beijing (2004) (in Chinese)

An Architecture for the Agricultural Machinery Intelligent Scheduling in Cross-Regional Work Based on Cloud Computing and Internet of Things Sun Zhiguo1,2, Xia Hui3, and Wang Wensheng1,2 1

Agricultural Information Institute, The Chinese Academy of Agricultural Sciences, Beijing, P.R. China 2 Key Laboratory of Digital Agricultural Early-warning Technology (2006-2010), Ministry of Agriculture, P.R. China 3 National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, P.R. China [email protected]

Abstract. The paper introduces the problems in china’s agricultural machinery information. We provide an architecture for the agricultural machinery intelligent scheduling in cross-regional work. We put forward constructing the private cloud of agricultural machinery with the aid of cloud computing technology, and forward agricultural machinery will link together through Internet of Things technology. We provide an information platform and simplified it to three components including information service system, communication line and monitoring front-end equipment machine carrying. We also describes two modes to realize the intelligent scheduling function of agricultural machinery cross-regional working. Keywords: Agricultural machinery, Cross-regional work, Intelligent scheduling, Cloud computing, Internet of Things, GPS, Bei-dou.

1 Introduction The agricultural mechanization is a key measure to improve efficiency of agriculture production. The agricultural mechanization of china has grown increasingly, presented a good situation of fast and sound development since 2000. Today, the agricultural machinery with high performance and big power and compound working keeps high speed growth, the structure of agricultural machinery equipment has made a remarkable improvement, and the level of farmland working mechanization has risen considerably. In recent years, the informationization construction of agricultural machinery has developed to some extent in our country, and information network services are further provided too. But because the informationization construction of agricultural machinery started later in China, the whole level is still lower, there exists differences especially in the development and utilization of agricultural machinery information resource, comparing with developed countries and other domestic industries. The D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 9–15, 2011. © IFIP International Federation for Information Processing 2011

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Z. Sun, H. Xia, and W. Wang

negative effects of unrestricted flows of agricultural machines begin to show gradually. The situation of gathering and loss of control may occur sometimes, and cause society instability incidents. The unrestricted flows of agricultural machines are as follows: 1. wasting energy; 2. lower working efficiency; 3. instability in service prices; 4. increasing traffic pressure; 5. the damage of fields water conservancy facilities resulted by repeat movement; 6. no farm machines and implements to hire in some remote areas. We will construct an advanced intelligent scheduling platform for agricultural machinery cross-regional working with advanced communication and information technologies such as Internet, mobile telephone, fixed-line telephone, 3G, GPS, Bei-Dou satellite navigation system, cloud computing and Internet of Things and so on, to implement the guidance and service of administrative departments to agricultural mechanization production, promote the restricted and efficient flows of agricultural machinery, improve the utilization and benefit of agricultural machinery, provide alldirectional services for agricultural machinery users and farm households.

2 Functions The platform can command and dispatch farm machines and implements to execute cross-regional working and accomplish the tasks of tillage and cultivation and harvest according to the factors such as crop mature time, weather, farm machines distribution in different areas of our country. It can realize various functions including inquiry of farm machines position, track review, information reception and release, state monitoring of farm machines, failure remote diagnosis of farm machines, inquiry of maintenance and oil supply sites, and measure of farmland area and estimation of crop yields.

3 Architecture Design 3.1 Design of Overall Information Network Architecture We will construct an advanced intelligent scheduling platform consisting of information system, database, users at all levels and farm machines for agricultural machinery cross-regional working. The information system and database will be constructed and monitored uniformly by central government with the aid of cloud computing technology and constructing the private cloud of agricultural machinery, namely agricultural machinery cloud that will control all computing resource of information system and storage resource of database. The system users consist of agricultural mechanization administrative departments from central government to local governments, service organizations of agricultural machinery and agricultural machine users. The agricultural mechanization administrative departments at all levels and service organizations of agricultural machinery can supervise the farm machinery and implements in their areas and realize the intelligent scheduling of cross-regional working by using the information system uniformly. The agricultural machine users can get related scheduling information and all kinds of service information with the system. The trends, static state and other information factors related to agricultural machinery production

An Architecture for the Agricultural Machinery Intelligent Scheduling

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including crop mature time in different areas of our country, farm machines distribution and state, current crop planting and disasters such as drought or waterlog will be stored in the database of agricultural machinery cloud, and the database can get information related to agricultural machinery production by network connecting to the databases of national departments of atmosphere, earthquake monitoring and water conservancy in time. With Internet of Things technology, farm machines and implements in all areas can communicate with datacenter of information service platform in wireless mode by configuration of monitoring front-end equipments machine carrying, and upload all kinds of information such as running state and geographical position automatically.

Fig. 1. The information network architecture

The information platform can be simplified to three components including information service system, communication line and monitoring front-end equipment machine carrying. 3.2 Design of Information Service System The customer level of information service system will consist of central system, local classification system, agricultural machinery service organization system and agricultural machine users system. The central system has two main functional modules which are system maintenance and resource distribution module and intelligent scheduling module of agricultural machinery cross-regional working. The management and maintenance of whole system are taken in charge by central government with the system maintenance and resource distribution module. The intelligent scheduling module of agricultural machinery cross-regional working can command and control farm machines and implements to execute cross-regional working and accomplish the tasks of tillage and cultivation and harvest, realize the functions including inquiry of farm machines position and track review. The local classification system can be divided into multi-level such as province level, ground level, county level and other higher and lower system according to the detailed condition in different areas. The systems at all levels have the similar functions; the main function module is intelligent scheduling module of agricultural

12


machinery cross-regional working that has similar function comparing with the scheduling module in central system. The agricultural machinery service organization system that is grass-roots organizations of agricultural machinery management mainly comprises two functional modules which are intelligent scheduling module of agricultural machinery cross-regional working and farm machines management module. The function of intelligent scheduling module of agricultural machinery cross-regional working is similar to that of central and local systems. The farm machines management module can provide data management for farm machines in the whole large-scale system. All farm machines registered in system should belong to a certain grass-roots agricultural machinery service organizations in principle, the service organizations should acknowledge and supervise the information validity of farm machines belonging to them. The agricultural machine users system can mainly provide some scheduling services and additional service automatically. The agricultural machine users or farm households can receive scheduling instructions automatically and answer back, and get some additional service interactively such as location navigation service, farm machines state alarm, failure remote diagnosis of farm machines and inquiry of maintenance and oil supply sites. 3.3 Design of Communication Lines and Monitoring Front-End Machine Carrying The users at all levels can connect to information platform through various Internet connection modes directly. They also can encrypt the data transmitting end-to-end with installation of SSL VPN considering the data confidentiality of the entire system. The devices used to connect to Internet can support all kinds of information terminals, and normal agricultural machine users can utilize the function such as failure remote diagnosis of farm machines by furnishing mobile video terminals such as cell phones. The farm machines and implements can connect to information platform and upload data by the suggesting three patterns as follow considering different situations of all parts of our country:

Fig. 2. Three patterns of connect to information platform and upload data


13

①

Installing GPS tracker or Bei-Dou tracker on the farm machines, and machines transmit data to datacenter by mobile network including GPRS, TD, WCDMA. It is the main connection pattern of the platform. Binding RFID on the farm machines, the agricultural machinery service organizations install mobile RFID reader in the areas in which signals are acceptable, and read the running state data of farm machines terminals needing management in their area of jurisdiction intently, and upload data by mobile network uniformly. The pattern adapts to the group working of farm machines and implements, for example, various farm machines and implements are combined together to form a comprehensive service farm machine group, these farm machines and implements typically move and are supervised uniformly. The farm machines and implements can upload data with handheld devices or netbooks after getting data manually. The pattern adapts to the farm machines and implements without automatic uploading conditions. It is a good supplement to the first pattern. The monitoring front-end equipment farm machine carrying is a small scale integration instrument installing on the farm machine, can integrate all kinds of sensors and data collection devices, and upload data automatically. For example, it can integrate GPS or Bei-Dou positioning module to get position information and record the movement tracks of farm machines and implements for intelligent scheduling of cross-regional working; it can integrate video camera to record the working state of farm machine users; it can integrate oil circuit sensor to get oil supply information, and upload data to agricultural machinery cloud, and perform remote computing combining with its movement state to match the oil supply time and best supply sites, then transmit the data to farm machine users as reference; it can integrate key running position sensor to monitor the working state and help making failure diagnosis; it can integrate metered sensor to record farm machines working distance.

②

③

Fig. 3. The monitoring front-end equipment

The front-end equipment machine carrying can send position information and various data of running state to agricultural machinery cloud continuously with Internet of Things in working time of farm machine, and provide decision making references and service information. If data transmission fails and overruns the preset time, the information platform system will automatically alarm and send cell phone message to farm

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machine users by the system, or the operators of agricultural machinery service organizations will contact with users by telephone or other measures to deal with the farm machine. 3.4 Design of Intelligent Scheduling of Agricultural Machinery Cross-Regional Working There are two modes as follows to realize the intelligent scheduling function of agricultural machinery cross-regional working: The Manual Deployment Mode. The deployment modes of systems at all levels are abstracted to a unified mode that is deployment between higher and lower. The flow chart is as follow:

The higher system

④

①

A lower system

②

③

B lower system

Fig. 4. The manual deployment mode

The meaning of flow chart is as follows:

① ②

The lower system of district A requests higher system to deploy farm machines to district A. The administrative personnel of higher system finds the lower system of district B has spared farm machines after reviewing the system data collected from all districts, then gives deployment instructions to the lower system of district B, and deploys X farm machines to district A The lower system of district B replies to higher system about the details of deployment, and dispatches spared farm machine starting off. The higher system sends the detailed deployment program to lower system of district A, then the lower system of district A implements the program, the flow of cross-regional deployment comes to an end.

③ ④

Intelligent study and automatic scheduling mode. Basing on prior consideration of manual deployment mode, the system has a scheduling mode of automatic matching. According to the scheduling requirements proposed by administrators of scheduling centers of system at all levels, the system automatically sends recommended program of automatic matching based on model algorithm to system scheduling centers at all levels considering the factors including weather and mature time, and scheduling administrators of system at all levels make comprehensive judgment according to


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existent experience and issue scheduling instruction. The system also has automatic study function, can correct the scheduling model continuously according to the final scheduling program implemented by scheduling administrators of system at all levels. To realize the intelligent scheduling model algorithm, the factors including weather and crop mature time should be considered; the smallest distance matrix of all deployment sites and the smallest path matrix relevant should be computed using Floyd algorithm; the tasks are assigned by sweep algorithm; the task routes are sorted by genetic algorithm; the existing research results related to multi-depot vehicle scheduling problem home and abroad need to be studied. The paper focus on architecture research, so do not analyze the detailed algorithm in depth hereon. Acknowledgements. The work is supported by the Academy of Science and Technology for Development fund project “intelligent search-based Tibet science & technology information resource sharing technology”, the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), and the special fund project for Basic Science Research Business Fee, AII (No. 2010-J-07).

References 1. 2.

3. 4. 5. 6.

7.

Li, X.-w., Zhang, S.-m., Li, Z.-l.: Agricultural mechanization information network for review and think. Agricultural Equipment & Technology 154, 4–6 (2009) (in Chinese) Wen, H.-h., Liu, L.-h.: Toward construction of the information to the problems and countermeasures. China Agricultural Machinery Safety Supervision 09, 24–25 (2008) (in Chinese) Zhang, Y., Liu, M.: Propel the development of agriculture mechanical information. Farm Machinery, 124–125 (March 2006) (in Chinese) Ding, W., Liang, C., Xia, M.-h: A Intelligent Public Transportation Scheduling System Based on GPS. China Computer & Communication, 36–37 (July 2009) (in Chinese) Zhang, Q.-z., Liu, B.-w., Li, J.-t.: Physical Distribution Monitoring System Based on Google Earth. Logistics Technology 206, 200–202 (2009) (in Chinese) Lang, M.-x.: Study on the Model and Algorithm for Multi-Depot Vehicle Scheduling Problem. Journal of Transportation Systems Engineering and Information Technology, 65– 68 (October 2006) (in Chinese) Zhao, L.-h.: Study on Vehicle Scheduling Model and Algorithm for City Multi-node Delivery. Logistics Technology, 91–93 (August 2007) (in Chinese)

A Comparative Study of Modified Materials of Acetylcholinesterase Biosensor Xia Sun1, Xiangyou Wang1,*, Wenping Zhao1, Shuyuan Du1, Qingqing Li1, and Xiangbo Han2 1 2

School of Agricultural and Food Engineering, Shandong University of Technology, College of Computer Science and Technology, Shandong University of Technology, Zibo 255049, Shandong Province, P.R. China [email protected]

Abstract. In this study, multi-walled carbon nanotubes (MWCNTs), gold nanoparticles (GNPs) and Prussian Blue (PB) were used for modifying glassy carbon working electrode (GCE) to construct acetylcholinesterase (AChE) biosensor respectively. Chitosan membrane was used for immobilizing AChE through glutaraldehyde cross-linking attachment to recognize pesticides selectively. Before the detection, the enzyme membrane was quickly fixed on the surfaces of modified electrode with O-ring to prepare an ampero-metric acetylcholinesterase biosensor for organophosphate pesticides. The fabrication procedures were characterized by cyclic voltammetry and amperometric i-t curve. The electrochemical behaviours of three modified sensors were compared, and the results showed that AChE-PB/GCE possessed higher oxidation peak current at a lower potential. Based on the inhibition of organophosphorus pesticides to the enzymatic activity of AChE, using dichlorvos as model compound, the sensitivity of three modified biosensors were compared, the results showed that the detection limit of AChE-PB/ GCE was lowest. Keywords: Biosensor; Acetylcholinesterase; Pesticide residue; Modified electrode.

1 Introduction Organophosphorus (OP) pesticides are widely used in agricultural production which leads to the most important environmental pollutants. Moreover, OP compounds inhibit acetylcholinasterase (AChE) that hydrolyses the neurotransmitter acetylcholine (ACh), often causing severe impairment of nerve functions of human or even death.[13] For these reasons, the development of rapid and efficient monitoring methods is very important. In the past years, many studies have focused on biosensors based on the enzymatic inhibition by the OP pesticides. They have the additional advantage of simplicity, rapidity, reliability, low cost devices and on site monitoring.[4] Generally speaking, the concentration of pesticides is monitored by measuring the change of oxidation current of thiocholine before and after exporsured to pesticides.[5-7] *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 16–24, 2011. © IFIP International Federation for Information Processing 2011

A Comparative Study of Modified Materials of Acetylcholinesterase Biosensor

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However, the oxidation generally requires high potential value on a suitable electrode.[8] In order to enhance the test sensitivity, decrease potential values and the electrochemical interference of other oxidable compounds, the use of some modified materials and methods have gained enormous attention in biosensor technology in recent years, such as multi-walled carbon nanotubes(MWNTs),[9-11] prussian blue (PB)[7,12-13] and gold nanoparticles(GNPs).[14-16] Most of these methods rely on enzyme immobilization directly onto the electrode surface, which cannot overcome the biofouling of the electrode surface, and would eventually lead to the deactivation of the biosensor or at least to worsening of the electrochemical response. Our previous investigation results have shown that using a replaceable membrane as support for the enzyme immobilization has many advantages, for example, enzyme membrane can be easily replaced when enzyme’s activity is lost.[9,17] Moreover, there are multiple options for analyte detection based on enzyme immobilization on the membrane (one electrode-multiple membranes-multiple enzymes).[18] This present work is a continuation of our previous investigations and focused on the comparative study of three modified (MWCNTs, GNPs and PB) materials to obtain higher sensitivity and stability biosensor for OP pesticides. The fabrication procedure was characterized by cyclic voltammograms and amperometric i-t curve, respectively. The electrochemical behaviours of three modified sensors and no modified AChE/GCE sensor were compared, and the results showed that the AChEPB/GCE obtained higher oxidation peak current at a lower work potential. Using dichlorvos as model compound, the sensitivity of three modified biosensors were compared, the results showed that the detection limit of AChE-PB/GCE was lowest. The AChE-PB/GCE biosensor exhibited good reproducibi-lity, stability and it was suitable for trace detection of OP pesticide residue.

2 Experimental 2.1 Apparatus Cyclic voltammograms and amperometric i-t curve were performed with CHI660D electrochemical workstation (Shanghai Chenhua Co., China). 10ml of electrochemical cell was made in our laboratory. The working electrode was glassy carbon electrode (d = 3mm) or modified glassy carbon electrode. A saturated calomel electrode (SCE) and platinum electrode were used as referenceand auxiliary electrodes, respectively. 2.2

Reagents

Acetylcholinesterase was purchased from Nuoyawei Biology Tech.Co. (Shanghai, China). Acetylthiocholine iodide (ATChI), glutaraldehyde (25%) and bovine serum albumin (BSA) were provided by Sigma. Cellulose nitrate microporous membrane was purchased from Hangzhou Rikang purification equipment co.,ltd (Hangzhou, China). Chitosan (95% deacetylation), phosphate buffer (PBS, pH 8.0) and other reagents were all of analytical grade. Dichlorvos was standard product. All the other chemicals were of analytical grade. Distilled water was used throughout for the preparation of solutions.

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2.3 Preparation of AChE Biosensors 2.3.1 Preparation of Chitosan Membrane A solution was prepared with 0.1 g chitosan added to 10 ml of acetate solution (1%, mass ratio), and the mixture was centrifuged for 5min in high-speed centrifuge at 3000rpm to remove insoluble particles. Finally, the pretreated cellulose nitrate microporous membrane was immersed in this sol for 12 h, and then immersed in phosphate buffer (PBS, 0.1 mol/l, pH 8.0) for 12 h, dried and stored for use.[19] 2.3.2 The AChE Immobilization A solution of 100μl of AChE liquid (100U/ml), 30.0μl of BSA (1.0%), 10μl of glutaraldehyde (5.0%), and 360μl of PBS (0.1mol/l, pH8.0) were mixed in a 1 ml of centrifuge tube. A chitosan membrane was immersed in it for 8h at 4oC. Finally, enzyme membranes was washed with PBS (0.1mol/l, pH8.0), immersed in PBS (0.1mol/l, pH8.0), and stored at 4oC before use.[17] 2.3.3 Electrode Modification (1) The preparation of MWCNTs/GCE 20μL of mixture of MWNTs, chitosan and glutaraldehyde were covered on a pretreated GCE with final contents of 0.12% (w/v), 0.48% (w/v) and 0.47% (v/v) respectively, and allowed for reaction at room temperature for 4 h. After being washed thoroughly with double distilled water, the obtained modified electrode was stored at 4oC before use. [20] (2) The preparation of AuNPs /GCE 0.01% HAuCl4 solution was heated to boiling, and quickly added 1 ml 1% sodium citrate. After 1min, the color of solution changed from yellowish to light rose red. Then the AuNPs solutions were stored in dark glass bottles at 4°C. After the working electrode was immersed in 10 ml of AuNPs solutions for 24 h at 4°C, the surface of working electrode was rinsed in double-distilled water for use.[21] (3) The preparation of PB/GCE A solution was a mixture of 2 mM K3[Fe (CN)6], 2 mM FeCl3, 0.1 M KCl, and 10 mM HCl, and the B solution was a mixture of 0.1 M KCl and 10 mM HCl. First, a potential of +0.4V was applied to the electrode in solution A for 60 s and then the electrode was transferred to solution B, and scanned by cyclic voltammetry from -0.05 and 0.35V at a rate of 50mV/s for 12 times. The electrode surface was rinsed with double-distilled water. Finally, the electrode was stored at room temperature.[22] 2.4 Electrochemical Detection of Pesticide The biosensor was tested with amperometric i-t curve (i-t) at a potential of 600 mV versus saturated calomel electrode (SCE). After 100μL of ATChI (15mg/ml) solution was injected into the cell, and the peak current was recorded as I0. The cell was washed with distilled water between measurements. For OP pesticide detection, the pretreated biosensor was first incubated in a given concentration of dichlorvos for 10 min, then it was transferred to the electrochemical cell of 10mL PBS (0.1mol/L, pH8.0), and 100μL of ATChI (15 mg/mL) was injected


19

after the current stabilized. The peak current was recorded as I1. The inhibition of pesticides was calculated as follows: I % = ( I 0 − I1 ) / I 0 × 100%

Where I% was the degree of inhibition related to the inhibitor concentration. I0 was the initial current of the biosensor which was measured without inhibitor in PBS (0.1mol/L, pH8.0). I1 was the current after the incubation in the PBS (0.1mol/L, pH8.0) with different concentrations of inhibitor.

3 Result and Discussion 3.1 Electrochemical Behavior of AChE-MWCNTs/GCE, AChE-AuNPs/GCE and AChE-PB/GCE Fig.1 showed the cyclic voltammograms of AChE-MWCNTs/GCE, AChEAuNPs/GCE and AChE-PB /GCE in the presence of ATChI (15mg/ml) in PBS (pH 8.0) at a scan rate of 100mV/s. After 100μl of ATChI (15mg/ml) was injected into PBS, AChE-GNPs/GCE identified an oxidation peak current of 45µA at 510mV, and the AChE-MWCNTs/GCE obtained an oxidation peak current of 22µA at 600mV, and the AChE-PB/GCE was an oxidation peak current of 90µA at 570mV respectively. The oxidation peak (curve a, b and c) came from the oxidation of thiocholine, hydrolysis product of ATChI, catalyzed by immobilized AChE. Fig.1 also showed that this peak current of AChE-PB/GCE (curve c) was much higher compared with AChE-MWCNTs /GCE and AChE-AuNPs/GCE. The phenomena was due to PB possess better electrocatalytic ability on the AChE. Whereas, the potential of AChEAuNPs/GCE shifted negatively compared with AChE-MWCNTs/GCE (curve a) and AChE-PB/GCE (curve c). It was likely because that AuNPs possessed inherent high electricity conducting ability, thus can provide a conductive pathway for electron 60 40

current(uA)

20 0

a

-20 -40

b

-60 -80 1000

c 800

600

400

200

0

E(V) Fig. 1. Cyclic voltammograms of enzyme biosensor modified. MWCNTs modified (a); GNPs modified (b); PB modified (c) in pH 8.0 PBS containing 100μL of ATChI(15mg/mL). Scan rate: 100mV/s.

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transfer and promote electrocatalysis reactions at a lower potential. At the same time, these three modified biosensor obtained oxidation peak current were comparable with that reported electrochemical biosensor at the same potential.[23-24] For this main reason were the use of chitosan membrane, which provided a biocompatible microenvironment around the enzyme molecule to stabilize its biological activity and prevented the enzyme leaking out from chitosan membrane effectively. Dual-layer membranes had synergistic effects towards enzymatic catalysis, thus, the oxidation peak current increased, which can improve detection sensitivity.

current(uA)

0 -20

b a

-40

c

-60 -80 0

20

40

60

80

100

120

time(s)

Fig. 2. Amperometric i-t curve of enzyme biosensor modified. MWCNTs modified (a); GNPs modified (b); PB modified (c) in PBS (0.1mol/L, pH8.0) after injected 100μL of ATChI (15mg/mL)

The current produced by AChE-MWCNTs/GCE, AChE-AuNPs/GCE and AChEPB/GCE catalyzing ATChI achieved to 22μA, 35μA and 80μA at 600mV repectively (Fig.1), which were according with the result tested by ampomeretric i-t (Fig.2), which indicated that we can also detect electrochemical behavior of enzyme biosensor with ampomeretric i-t. 3.2 Effect of Phosphate Buffer pH on AChE-MWCNTs/GCE, AChE-AuNPs/GCE and AChE-PB/GCE The effect of phosphate buffer pH value on the peak currents was shown in Fig.3. The current response of three modified biosensors increased with an increase of pH value up to 7.5, and then the AChE-MWCNTs /GCE current decreased at higher pH value, whereas, the current of AChE-AuNPs/GCE and AChE-PB/GCE continue increase until pH value arrive to 8.0. It could be concluded that the values of the peak current of biosensors changed with the different pH in the range of 5.0 to 8.5. Obviously, the maximum response of peak current appeared at pH 7.5 about AChE-MWCNTs/GCE, and the others at pH 8.0. The phenomena was due to the pH value of electrolyte, which had great influence on the activity of enzyme, which led to the change of the anodic peak current at these biosensors.


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current µA

）（

80 GNPs

60

PB

40

MWCNT s

20 0 4.5

5

5.5

6

6.5

7

7.5

8

8.5

pH

Fig. 3.The influence of pH on the peak current of enzyme biosensor modified with MWCNTs, GNPs and PB respectively

3.3 Effect of ATChI Concentration on AChE-MWCNTs /GCE, AChE-AuNPs /GCE and AChE-PB/GCE Fig.4. showed the effect of different ATChI concentration on anodic peak current of AChE-MWCNTs/GCE, AChE-AuNPs/GCE and AChE-PB/ GCE. The peak current all increase when the ATChI concentration was less than 15mg/l, whereas the peak current have no change with further the increasing of the concentration of ATChI. It was likely because that the velocity of enzyme catalyzing substrate reaches to the equilibrium when the substrate added to some concentration, so subsequent increased the substrate concentration, the velocity of enzyme catalyzing substrate did not increase. In this work, the ATChI concentration of 15mg/l was selected.

current (µA)

100 80

MWCNT s

60

PB

40

GNPs

20 0 0

10

20

30

40

AT ChI(mg/L)

Fig. 4. The influence of ATChI concentration on the peak current of enzyme biosensor modified with MWCNTs, GNPs and PB respectively

3.4 Effect of Incubation Time on Inhibition As shown in Fig.5, OP pesticides displayed increasing inhibition to AChE with incubated time. When the incubated time was longer than 10 min the three curves all trended to maintain a stable value, which indicated that the binding interaction with active target groups in enzyme could reach saturation. This change tendency of the

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100 90 80 70 60 50 40 30 20 10 0

inhibition

%

）（

MWCNT s PB GNPs

1

3

5

7 time

10 12 15 20 25

（min）

Fig. 5. The influence of Pesticide inhibition time on the peak current of enzyme biosensor modified with MWCNTs, GNPs and PB respectively

peak current value showed the alteration of enzymatic activity, which resulted in the change of the interactions with its substrate. In this work, the three biosensors optimum incubation time of 10 min was selected. 3.5 Determination of Pesticides After AChE-MWCNTs/GCE, AChE-AuNPs/GCE and AChE-PB/GCE were incubated in the standard solution of dichlorvos at a certain concentration for 10 min respectively, the inhibition rate (calculated by the change of peak current) of these three modified biosensors and the logarithm of dichlorvos concentration all had a certain linear relationship in some range. The detection limit and linear range of AChEMWCNTs/GCE and AChE-AuNPs/GCE were shown in Tab.1. The results showed that the detection limit of AChE-PB/GCE was lowest. The phenomena were indicated that the electrode modified materials played an important role on the sensitivity of enzyme biosensor. Table 1. The detection limit of three modified biosensors of dichlorvos pesticides modified biosensor

linear range

AChE-PB/GCE AChE-GNPs/GCE AChE-MWCNTs/GCE

10ng/l~10μg/l 50ng/l~10μg/l 5μg/l~50μg/l

equation of linear regression I=32.3lgc-10.9 I=22.804lgc-6.3489 I=48.853lgc+10.927

equation of linear regression 0.9968 0.9928 0.9921

detection limit 2.5ng/l 30ng/l 1ug/l

3.6 Precision of Measurements and Stability of Biosensor The precision intra-assay of the three biosensors was evaluated by assaying three enzyme membranes on the same electrode for ten replicate determinations after exposure to a certain concentration pesticides respectively. Similarly, the inter-assay precision was estimated by assaying three enzyme membranes on six different electrodes. The average relative standard deviation (R.S.D.) of intra-assay and inter-assay were


23

found to be 5.1 and 4.27% of AChE-MWCNTs/GCE, 5.2 and 3.1% of AChEAuNPs/GCE and 4.8 and 3.5% of AChE-PB/GCE respectively, which indicated these three modified biosensors are all acceptable re-producibility.

4 Conclusion In this paper, three materials modified have been used for the fabrication of amperometric AChE biosensors. These AChE biosensors all introduce the chitosan membrane to immobilize AChE, the results have shown that chitosan membrane prevent leakage of the enzmye, improve the activity of immobilization enzyme, and can immobilize sufficient amount of AChE. The fabrication procedures have been characterized by cyclic voltammetry and amperometric i-t curve. The electrochemical behaviours of three modified sensor have been compared, and the results showed that AChE-PB/GCE possess higher oxidation peak current at a lower potential. Using dichlorvos as model compound, the sensitivity of three modified biosensors have been compared, the detection limit of AChE-PB/GCE is lowest. This study indicates we can improve the sensitivity of enzyme biosensor by the selection of the modified materials of electrode and realize the trace detection of OP pesticide residue. Acknowledgments. This work was supported by the National Natural Science Foundation of China (No.30972055), Scientific and Technological Project of Shandong Province (No.2008GG10009027), and the Natural Science Foundation of Shandong Province (No. Q2008D03).

References 1. Laschi, S., Ogończyk, D., Palchetti, I., Mascini, M.: Enzym. Microb. Technol. 40, 485– 489 (2007) 2. Ivanov, A.N., Lukachova, L.V., Evtugyn, G.A., Karyakina, E.E., Kiseleva, S.G., Budnikov, H.C., Orlov, A.V., Karpacheva, G.P., Karyakin, A.A.: Bioelectrochem. 55, 75–77 (2002) 3. Du, D., Chen, S.Z., Cai, J., Zhang, A.D.: Biosens. Bioelectron. 23, 130–134 (2007) 4. Arduini, F., Ricci, F., Tuta, C.S., Moscone, D., Amine, A., Palleschi, G.: Anal. Chim. Acta. 58, 155–162 (2006) 5. Ramírez, G.V., Fournier, D., Silva, M.T.R., Marty, J.L.: Talanta. 74, 741–746 (2008) 6. Wu, H.Z., Lee, Y.C., Lin, T.K., Shih, H.C., Chang, F.L., Lin, H.P.: J. Taiwan Institute Chem. Eng. 40, 113–122 (2009) 7. Pchelintsev, N.A., Vakurov, A., Millner, P.A.: Sens. Actuators B 138, 461–466 (2009) 8. Pingarrón, J.M., Sedeño, P.Y., Cortés, A.G.: Electrochimica Acta 53, 5848–5866 (2008) 9. Sun, X., Wang, X., Zhao, W.: Sensor. Lett. 8, 247–252 (2010) 10. Ivanov, Y., Marinov, I., Gabrovska, K., Dimcheva, N., Godjevargova, T.: J. Mol. Catal. B: Enzym. 63, 141–148 (2010) 11. Chen, J., Du, D., Yan, F., Ju, H.X., Lian, H.Z.: Chemistry A European Journal 11, 1467– 1472 (2005) 12. Li, J.P., Wei, X.P., Yuan, Y.H.: Sens. Actuators B 139, 400–406 (2009) 13. Sun, X., Wang, X.: Biosens. Bioelectron 25, 2611–2614 (2010)

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14. Du, D., Chen, S.Z., Song, D.D., Li, H.B., Chen, X.: Biosens. Bioelectron. 2, 475–479 (2008) 15. Kim, G.Y., Shim, J., Kang, M.S., Moon, S.H.: J. Hazard. Mater. 156, 141–147 (2008) 16. Shulga, O., Kirchhoff, J.R.: Electrochem. Commun. 9, 935–940 (2007) 17. Sun, X., Wang, X.Y., Liu, Z.: Int. J. Food Eng. 4, 4 (2008) 18. Marinov, I., Gabrovska, K., Velichkova, J., Godjevargova, T.: Int. J. Biol. Macromol. 44, 338–345 (2009) 19. Qiang, Z., Chen, Y., Guo, H., Liu, J.: J. Dong Hua Univ. 33, 212–215 (2007) 20. Du, D., Huang, X., Cai, J., Zhang, A.D., Ding, J.W., Chen, S.Z.: Analytical and Bioanalytical Chemistry 387, 1059–1065 (2007) 21. Agüí, L., Peña-Farfal, C., Yáñez-Sedeño, P., Pingarrón, J.M.: Talanta. 74, 412–420 (2007) 22. Jin, G., Hu, X.: Chinese Journal of Analysis Laboratory 27, 14–17 (2008) 23. Wu, H.Z., Lee, Y.C., Lin, T.K., Shih, H.C., Chang, F.L., Lin, H.P.P.: J. Taiwan Institute Chem. Eng. 40, 113–122 (2009) 24. Yin, H.S., Ai, S.Y., Xu, J., Shi, W.J., Zhu, L.S.: J. Electroanal. Chem. 637, 21–27 (2009)

A Detection Method of Rice Process Quality Based on the Color and BP Neural Network Peng Wan1,2,∗, Changjiang Long1, and Xiaomao Huang1 1

2

College of Engineering, Huazhong Agricultural University, Wuhan, P.R. China College of Biological and Agricultural Engineering, Jilin University, Changchun, P.R. China [email protected], [email protected], [email protected]

Abstract. This paper proposed a detection method of rice process quality using the color and BP neural network. A rice process quality detection device based on computer vision technology was designed to get rice image, a circle of the radius R in the abdomen of the rice was determined as a color feature extraction area, and which was divided into five concentric sub-domains by the average area, the average color of each sub-region H was extraction as the color feature values described in the surface process quality of rice, and then the 5 color feature values as input values were imported to the BP neural network to detection the surface process quality of rice. The results show that the average accuracy of this method is 92.50% when it was used to detect 4 types of rice of different process quality. Keywords: Process quality, Rice color, BP neural network, Rice.

1 Introduction Rice is one of the most important crops in the world, the staple food of about half of the world's population is rice. The harvested paddy needs be processed into rice for human consumption by the processes of huller, mill, polishing and so on. The evaluation standards of rice process quality including grain shape, appearance and color, chalky, fragmentation rate, et al. The process quality of rice is one of the most important factors to determine the appearance quality and the selling price of rice. In the process of rice, the process quality of rice often judged by skilled workers, but due to people's subjective factors, it is difficult to describe accurately the results of the process quality of rice. With the development of science and technology, image analysis technology is widely used to detect and evaluate the rice quality[1][2][3][4]. The color of rice is one of the main factors of evaluating the quality[5]. While detecting the rice quality by the color features, people adopt more RGB color space and HIS color space; in addition, L* a* b* color space is also commonly used to extract the color feature value[6][7]. Since Rumelhart and others[8] proposed the back propagation algorithm, neural networks are widely used in many fields of agriculture. Majumdar S., D.S. Jayas and ∗



26

P. Wan, C. Long, and X. Huang

others[9] identified the type of grain by neural network system based according to the characteristics of grain morphology; Kazuhiro Nakano[10] used the neural networks to identify the quality of Apple's appearance, and the experiments showd that the BP neural network can classify the apple into three classes by the color features of the apple. The purpose of this paper is to propose a method of extracting rice color feature values, and detect the process quality of rice according to color feature values and through artificial neural network.

2 Materials and Methods 2.1 Rice Samples The rice samples were Wan Chang Rice, produced in Changchun, Jilin Province. The samples of rice grain were processed by huller, milling, polishing and other processes, and then respectively obtained the rice samples with 500g of four different forms: brown rice (BR), the first process rice (FR), the second process of rice (SR), polished rice (PR), and then packaged in sealed bags and kept in the shade. 2.2 Computer Vision System In order to obtain images of rice grains, the paper designs of the rice quality detection vision system for collection the images of rice samples. 2.3 Rice Image Processing To extract the color features of rice, rice needs to be extracted from the background image firstly. The process flow chart of a rice image was in Figure 1. The detection flow chart of rice process quality by image analysis was shown in Figure 2. Rice image acquisition

Graying image Threshold segmentation Save the rice images

Noise Cancellation Image contour extraction Seed filling

Segment rice image from the background

Fig. 1. The processing flow chart of rice image

A Detection Method of Rice Process Quality Based on the Color and BP Neural Network

27

Rice image acquisition Rice image pre-processing Extraction color values of rice image Rice image pre-processing Color space conversion Calculating color feature value of rice sample Using BPNN to detect the color of rice samples

Fig. 2. The detection flow chart of rice process quality by image analysis

2.4 Color Extraction For extracting the color values of rice image, we must first identify the color region in the abdomen of the rice. In order to guarantee every rice’ color extraction region contains the same number of pixels, this paper detected the equal area in the abdomen of the rice. This paper firstly calculates the cancroid of rice image by image processing, then an extraction region of the color values of rice image (CA, Color Area) is identified, which has the centroid of rice image and a circle of radius R. The extraction region of the color values of rice image (CA) must be in the outline of the rice image. The pixel coordinates of the rice image are expressed as {(xi, yi) | 0 ≤i≤ M, M is the pixel image points}, and the centroid coordinate of the rice image is (X, Y), then: 1 M ⎧ ⎪ X = M ∑ xi ⎪ i =0 ⎨ M 1 ⎪Y = ∑ yi ⎪⎩ M i =0

The color extraction regional diagram of rice image is in Figure 3.

Fig. 3. The color extraction region diagram of rice image

(1)

28


The extraction region of the color values of rice image (CA) was divided into 5 equal sub-regions (Sa, Sub area) by the concentric circles which have a centroid coordinate(X, Y) and ranging radius. The sub-regions have the number for the Sai (i = 1,2, ... ... 5) from the inside to the outside of the circle. Set the concentric circles in the CA have the radius of ri(i = 1,2, ... ... 5), the sketch map of 5 sub-regions in CA is shown in Figue 4.

Fig. 4. The sketch map of 5 sub-regions in CA

The relationship between the radiuses ri of the concentric circles and the radius R of the rice color extraction region (CA) is:

ri =

i R 5

(2)

In this paper, the average value of the pixel color values in sub-regional Sai of the CA is the color value of sub-regional Sai, and then the color of every rice can be described by 5 color values extracted from the CA. 2.5 Color Conversion The rice images obtained by the CCD are based on RGB color space. HSI color model is based on the human visual, which describe the color by using the Hue(H), Saturation(S) and Intensity(I) to sort. As HSI color mode is related with the hardware features, and is little sensitivity to the light source. This paper describes the rice color by using the HSI color system. Firstly, the RGB color values of rice pixel in CA are extracted, and then the RGB color values are converted to HSI color values. According to the characteristics of HSI color system, this paper uses the hue values H as the color characteristic values detecting the rice process quality. As the rice color extraction area (CA) in this paper is divided into 5 equal sub-regions, the color characteristic values of each sub-region is set to Hi(i = 1,2, ... ... 5), then each rice color feature value could be described as follow:

，，，，

H * = ( H1 H 2 H 3 H 4 H 5 )

(3)


29

2.6 The Identification Method of Rice Process Quality In this paper, BP neural network model is used to detect the process quality of rice. The rice samples of four forms are respectively selected to be used to image analysis, and to obtain the color feature value for making up of the sample set of the neural network training set. Suppose the detection values of brown rice (BR), first process rice (FR), second process rice (SR), polished rice (PR) are 0 1 2 3, and then the goals set of the neural network training set is (0, 1, 2, 3). Other 40 full rice are respectively selected from the rice samples of four forms to obtain the color feature value for making up of the detection set of the neural network. The BP neural network in this paper has three layers, the number of input layer neurons is 5, that is the number of the color feature values of rice samples; the number of output layer neuron is 4, and the output signal is (0, 1, 2, 3), and which respectively denotes the rice sample of BR, FR, SR, PR; the number of hidden layer neurons is confirmed according to the accuracy of the test results by using MATLAB software and test set. Neural network identification function is the logistic function:

、、、

f (x ) =

1 1 + exp(− x)

(4)

3 Results and Discussion 3.1 Obtain the Images of Rice Samples First of all, 120 rice are respectively selected from the 4 rice samples which have different process grade, then the rice images are obtained by the rice quality detection vision system. The rice images of four forms samples are shown in Figure 5.

(1)

(2)

(3)

(4)

Fig. 5. Rice sample images of different forms. (1)brown rice samples (BR); (2)the first process rice samples (FR); (3)the second process rice sample (SR); (4)polished rice samples (PR).

30


From Figure 6, brown rice(1) has the hard cuticle on the external layer, so brown rice shows a different color from the rice, and the outer layer of the brown is smooth; After milling, brown rice is processed into the initial processing rice samples(2), and as cutting through the rice milling machine, the outer layer of brown rice most of the stratum corneum epidermidis is cut, thus the entire outer layer of brown rice puts up mixture colors. After the second milling, brown rice is processed into secondary processing rice samples(3), then the outer layer of the stratum corneum epidermidis of whole rice is almost cut and shows the color of the rice, and at the same time, the outer layer of the rice will produce more fine particles, the surface of the rice is not smooth; After the second milling process, rice is polished into rice sample(4) by polishing processing, the fine particle layer of rice surface is removed, and the rice shows glossy color. 3.2 Rice Image Processing The goal image of rice can be obtained from the rice image through the rice image process, and the object images of rice are shown in Figure 6.

(1)

(2)

(3)

(4)

Fig. 6. The object images of rice samples (1)object image of BR; (2) object image of FR; (3) object image of SR; (4) object image of PR

3.3 Extract the Color Feature Value of Rice After the rice goal image was separated from the background, the color extraction area(CA) of the rice is firstly identified, and then the color feature values are extracted. The schematic diagram of the color extraction area (CA) of rice is shown in Figure 7. The radius R of the largest circle (CA) is 60, the CA is divided into five equal parts by the red circles, and code-named of the rice color feature extraction subregions from the inner circle to the cylindrical are Sa1, Sa2, Sa3, Sa4, Sa5.


31

Fig. 7. Schematic diagram of the color feature extraction region

The color feature values of each 120 samples in four form rice are extracted, and the relationship between the image pixels contained in the color feature extraction region of rice image and the image pixels contained in the rice image is shown in Table 1. Table 1. The relationship of the image pixels contained in the equal portion circles of the rice image Color extraction region pixel points

Sa1

Sa2

Sa3

Sa4

Sa5

CA

2286

2272

2270

2264

2264

11356

percentage of total CA

20.13

20.01

19.99

19.94

19.94

100%

percentage of total image (%)

5.19

5.16

5.16

5.14

5.14

25.79

prompt: the rice sample images have an average 44016 pixels.

From the table, the color extraction area (CA) contains 11 356 pixels; the pixels contained in the sub regions were 2286, 2272, 2270, 2264, 2264 and the error between the sub-region is less than 22 pixel; the percentages of total CA were 20.13%, 20.01%, 19.99%, 19.94%, 19.94%, the error is less than 0.19%; rice image contains an average of 44 016 pixels, the pixels points in CA is 25.79% of total pixel points of rice image; the percentages of the pixel points in sub-regions were 5.19%, 5.16%, 5.16%, 5.14%, 5.14% of the average total pixel points of rice images, the error is less than 0.05%. Therefore, the method of the division of the rice color extraction region into 5 equal sub-regions can insure the rice pixel points in every color feature extraction sub-region are equal. 3.4 Variation Rules of Color Feature Values The color feature values were extracted from 120 rice samples for four forms of rice respectively, and then transformed the color feature value from the RGB color space into the HSI color space, and the average values of the color characteristics H of four forms of rice samples were shown in Table 2.

32

P. Wan, C. Long, and X. Huang Table 2. The color characteristics H for four forms of rice samples The color feature value of the color featureextraction region

rice sample BR FR SR PR

Sa1

Sa2

Sa3

Sa4

Sa5

1.3624 3.1121 3.5502 3.9702

1.8812 2.5987 2.9942 3.6774

1.8509 2.5019 3.1421 3.0815

1.9821 2.2124 2.9278 3.7286

1.4798 2.5013 2.6696 3.1869

、、

From the Table 2, there are some variation rules between the color feature values H of 4 rice samples. The color feature values Hi(i=1 2 …… 5) of the same rice samples of different process methods don’t have significant variation rules, which are the same with the different structure and distribution of the composition in rice and the milling and polishing process in the different regions of the rice surface. But among the different forms of rice samples, the color feature values Hi(i=1,2, ... ... 5) increased significantly on the whole. when the rice sample is brown rice, H values are in the range[1.3624, 1.9821]; when brown rice are milled though first process, H values are in the range[2.2124, 3.1121]; after the second milling process, H values are in the range[2.6696, 3.5502], after the rice polished, H values are in the range[3.0815, 3.9702]. Obviously, this is the same with the removal process of the cuticle layer and the aleurone layer on the surface of the rice by milled and polished. The distribution histogram of the color feature values Hi (i = 1,2, ... ... 5) of 4 rice samples is shown in Figure 8. 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

Sa1

Sa2 BR sample

Sa3 FR sample

Sa4 SR sample

Sa5

PR sample

Fig. 8. The relationship between the color feature values H of 4 rice samples

From the figure 8, as for the different forms of rice samples in the same color feature extraction sub-region, the color feature values H change with the milling process and present a growing trend. Consolidated Table 2 and Figure 8, rice samples show the appearance of different colors with the rice milling process; and the color feature values of rice samples show some variation rules.


33

3.5 Identification of Rice Process Quality The train set is established adopting the color feature values H and detection values of 100 rice samples of 4 forms to train the BP neural network, and then the BP neural network is verified through the color feature values H and detection values of 50x4 rice samples of 4 forms, and the detection result of the number of different hidden layer neurons is shown in Table 3. Table 3. The color detection results of the rice sample using BP neural network rice samples BR FR SR PR

the number of rice accuracy rates(%) the number of rice accuracy rates(%) the number of rice accuracy rates(%) the number of rice accuracy rates(%)

The number of hidden layer neurons and the detection results Sa1

Sa2

Sa3

Sa4

Sa5

45 90.00 45 90.00 40 80.00 43 86.00

44 88.00 48 96.00 41 82.00 45 90.00

47 94.00 48 96.00 44 88.00 46 92.00

46 92.00 46 92.00 44 88.00 45 90.00

47 94.00 43 86.00 41 82.00 46 92.00

From the table, when the number of hidden layer neuron is 15, the rice samples’ overall accurate rate identification is highest, the accurate rate of brown rice is 94.00%, and the accurate rate of first process rice is 96.00%, the accurate rate of second process rice is 88.00%, and the accurate rate of the polished rice is 92.00%, the overall identification accuracy rates is 92.50%. From above mentioned, detect the rice process quality can achieve a satisfactory result by constructing the 3 layers BP neural network with 5 neurons in the input layer, 15 neurons in the hidden layer, 4 neurons in the output layer, and discriminate function of the logistic-type function, and using the rice color feature values H extracted from the surface of the rice samples.

4 Conclusions In this paper, the method of detecting rice process quality was verified by experiments based on the color and BP neural network. First of all, the rice images was obtained, then definite color feature extraction region, and then the color feature extraction region is divided into five color feature extraction sub-regions of the same area with concentric circles of different radius. The color values H of the color feature extraction sub-regions are regarded as the color feature value of the rice, and finally a BP neural network of three layers is adopted to detect the process quality of the rice. The experiment’s results show that accuracy rate of this method of extracting the hue values of the rice image to detect the process quality of the rice is 92.5%.

34


References 1. Hou, C., Seiichi, O., Yasuhisa, S., et al.: Application of 3D-Microslicing image processing system in rice quality evaluation. Transactions of The Chinese Society of Agricultural Engineering 17(3), 92–95 (2001) 2. Ling, Y., Wang, Y., Sun, M., et al.: A machine vision based instrument for rice appearance quality. Transactions of The Chinese Society of Agricultural Machinery 36(9), 89–92 (2005) 3. Wan, Y.N., Lin, C.M.: Rice quality classification using an automatic grain quality inspection system. Transaction of ASAE 45(2), 379–387 (2002) 4. Abdullah, M.Z., Guan, L.C., Lim, K.C.: The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. Journal of Food Engineering (61), 125–135 (2004) 5. Shang, Y., Hou, C., Chang, G.: Automatic detection of yellow-colored rice using image recognition. Transactions of the Chinese Society of Agricultural Engineering 20(4), 146– 148 (2004) 6. Cai, J.: An analysis of color models and criteria for their application to quality test of farm products. Journal of JiangSu University of Science and Technology 18(5), 22–25 (1997) 7. Vizhanyo, T., Felfoldi, J.: Enhancing color differences in images of diseased mushrooms. Computers and Electronics in Agriculture 26(2), 187–198 (2000) 8. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by backpropagation errors. Nature (323), 533–536 (1986) 9. Majumdar, S., Jayas, D.S.: Classifieation of cereal grains using machine vision: Morphology models. Trans. of the ASAE 43(6), 1669–1675 (2000) 10. Nakano, K.: Application of neural networks to the color grading of apples. Computers and Electronics in Agriculture 18, 105–116 (1997)

A Digital Management System of Cow Diseases on Dairy Farm Lin Li1, Hongbin Wang2, Yong Yang3, Jianbin He1, Jing Dong1,*, and Honggang Fan2 1

School of Animal Husbandry and Veterinary Medicine, Shenyang Agricultural University, 110866 Shenyang, P.R. China [email protected], [email protected] 2 School of anima Medicine l, Northeast Agricultural University, 150030 Harbin, P.R. China 3 School of Information and Electrical Engineering, Shenyang Agricultural University, 110866 Shenyang, P.R. China

Abstract. A digital management system of cow diseases is presented in this paper, which based on standardization disease management framework. It can manage dairy cow disease from each stage including cow file creation, routine monitoring, disease prevention and control. Integrate electronic medical records was set up, which based on medical records include cow basic information and routine monitoring results and disease prevention information and can implement statistical analytic function of disease rate and guide cow immunization. The Unique numbers and integrated medical records information of every cow will lay the foundation for food of animal origin traceability. This system includes four subsystems, cow basic information management subsystem, cow individual health monitoring and evaluation subsystem, cow electronic medical records subsystem and cow disease prevention and control subsystem. With the help of system analysis and software design techniques, it is can manage cow disease on dairy farm effectually. Keywords: Digital management, cow diseases, dairy farm.

1 Introduction In china, dairy production specifically in general is of great importance. There has been a good trend for the development of cow husbandry in recent years. However, milk and meat yield per cow tend to remain low, although total production has increased, mainly due to increased cow numbers. The reasons are manifold but the main is various kinds of diseases that are ineffective management due to short of disease system of administration. In some economically developed countries, information technology (IT) continues to develop rapidly and is widely and successfully employed in the dairy cattle sector. Large central computers with millions of cow files, operated by cow diseases control program, have been operational for decades to provide the farmers with information (Xiong B H, et al., 2005; Nuthall, P, et al., 2004; Warren, M, et al.2000). Data *



36

L. Li et al.

bases are also increasingly used in a decentralized way on low cost personal computers, by farmers and farm advisors, in the so-called management information systems. Veterinary practitioners use such systems to support a new methodology for safeguarding cow health under the prevailing intensive production conditions (Vaarst, M, et al., 2006; Hamilton, C, et al., 2006; Nyman, A, et al.2007). In this paper, we built a digital management system of cow diseases that combining computer technology, network technology and information management, it will prevent and control disease effectually and promote the economy of dairy farm significantly.

2 Design of the Digital Management System of Cow Diseases The digital management system of cow diseases is a network system that combines B/S structure and ASP techniques. B/S structure has low requirement for user’s hardware with high degree of information resource of expansibility. The users’ working interface is realized by the universal browsers and their needs can be satisfied clicking Demand analysis

User needs

Planning stage

Feasibility study

Design

Functional Design

Development stage

Architecture design Interface Design Data Structure Realize

Manage information

cow

basic

Application and maintain

Manage general health indicators of individual cows Cow digital medical records Prevention support

and

Application Maintain

Fig. 1. The route of the digital management system of cow diseases

decision

A Digital Management System of Cow Diseases on Dairy Farm

37

The Database of the System

Tracking manage database

Basic information management

General indicators

health

Electronic records

medical

Prevention control

Fig. 2. The Database of Cow Disease Digitization Management Platform

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and

38

L. Li et al.

the mouse. The main working logic realized in the server with little of it done in browser. The load in the client is simplified, decreasing the cost and working load of system maintenance in this way. It possesses five structures which are data storage layer, data service layer, safe layer, business layer and user service layer. Every layer was design by Object Oriented, duplication of groupware make data layer, safe layer and business layer flexibility. The route of the system is shown in fig.1. The system working platform adopts window 2003 server and database utilizes SQL Server2000. The designing method uses New Orleans designing mode, which classifies designs of database into four stages: analysis of needs, conceptual design, logical design and physical design. The Database of Cow Disease Digitization Management Platform is shown in fig.2. It is a cow tracking Management Database, which includes cow basic information management database, cow general health indicators database, cow electronic medical records management database, cow disease prevention and control database. The digital management system construction framework is a whole of many elements, which integrates information collection, communication, possessing and so on, the purpose is to provide technology and organization of cow information and security. Its main function is to collect information of cow diseases, processing, storage and analysis by feedback. The frame of system is shown in fig.3.

3 Implement and Function of the System This system includes four subsystems, which are cow basic information management subsystem, cow individual health monitoring and evaluation subsystem, cow electronic medical records subsystem and cow disease prevention and control subsystem. These functions were come true that including dairy farm management, cow information management, routine monitoring, medical records management, disease prevention, drug management, user information management and statistical analysis. The function of system is shown in fig.4. Standardization and applicable disease management framework has been built. It can manage dairy farm from each aspects including cow files creation, routine monitoring, disease prevention. Since the cow come in dairy farm, this system creates cow record and monitor cow health and evaluate abnormal index in whole breed management process dynamically. Integrate electronic medical records that can guide routine monitoring and Support decision making by statistical analysis was set up. It based on medical records include cow basic information and routine monitoring results and disease prevention information. Digitalization management of electronic medical records implements statistical analytic function of disease rate and can guide cow immunization and helminthicide. It is the core of cow disease control and supports user to obtain complete and precise information of disease and supply clinical decision service. The Unique numbers and integrated medical records information of every cow will lay the foundation for food of animal origin traceability. Cow routine monitoring content is divided to routine inspection, physiology monitoring, performance monitoring, ketone monitoring,

A Digital Management System of Cow Diseases on Dairy Farm

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Fig. 4. Function of the digital management system of cow diseases

parasite monitoring by analyzing causative agent and protective step of cow diseases, it settles foundation for cow health management.

4 Conclusion The digital management system of cow diseases that was created implements routine monitoring standardization, applicable and integrity electron case file, disease prevention systematization. It can manage dairy cow disease from each stage and ensures cow health and raises output and quality of milk, settle the foundation of foods of animal origin traceability. With the help of system analysis and software design techniques, it is can manage cow disease on dairy farm effectually. These will bring evident economic returns.

Acknowledgement Funding for this research was in part provided by china postdoctoral science foundation (NO.20090461189), the postdoctoral fund of Shenyang Agricultural University,

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L. Li et al.

Research fund for young teachers of Shenyang Agricultural University, Dr. Start Fund of Liaoning Province, P. R. China. The authors are grateful to the Shenyang Agricultural University for providing conditions with finishing this research.

References 1.

2. 3. 4.

5.

6.

7.

Xiong, B.H., Qian, P., Luo, Q.Y., Lv, J.Q.: Design and realization of solution to precision feeding of dairy cattle based on single body status. J. Transaction of the Chinese Society of Agricultural Engineering 21, 118–123 (2005) Nuthall, P.: Case studies of the interactions between farm profitability and the use of a farm computer. J. Comput. Electron. Agric. 42, 19–30 (2004) Warren, M., Soffe, R., Stone, M.: Farmers, computers and the internet: a study of adoption in contrasting regions of England. J. Farm Manage. 11, 665–684 (2000) Vaarst, M., Bennedsgaard, T.W., Klaas, I., Nissen, T.B., Thamsborg, M., Ostergaaerd, S.: Development and daily management of an explicit strategy of nonuse of antimicrobial drugs in twelve Danish organic dairy herds. J. Dairy Sci. 89, 1842–1853 (2006) Hamilton, C., Emanuelson, U., Forslund, K., Hansson, I., Ekman, T.: Mastitis and related management factors in certified organic dairy herds in Sweden. J. Acta Vet. Scand. 48, 25– 30 (2006) Nyman, A., Ekman, T., Emanuelson, U., Gustafsson, A.H., Holtenius, K., Persson Waller, K., Halleǹ Sandgren, C.: Risk factors associated with the incidence of veterinary-treated clinical mastitis in Swedish dairy herds with a high milk yield and a low prevalence of subclinical mastitis. J. Prev. Vet. Med. 78, 142–160 (2007) Nodtvedt, A., Bergvall, K., Emanuelson, U., Egenvall, A.: Canine atopic dermatitis: validation of recorded diagnosis against practice records in 335 insured Swedish dogs. J. Acta Vet. Scand. 48, 1–7 (2006)

A General Agriculture Mobile Service Platform Hu Haiyan1,2,* and Su Xiaolu1,2 1

Key Laboratory of Digital Agricultural Early-warning Technology, Ministry of Agriculture, The People’s Republic of China 100081 2 Agricultural Information Institute of Chinese Academy of Agriculture Science, Beijing 100081, P.R. China Tel.: +86-10-82106263; Fax: +86-10-82106263 [email protected]

Abstract. Most of today’s information services on the web are designed for PC users. There are few services fit to be accessed by mobile devices. In the countryside of China, most of the mobile phone users can not access the Internet. For this reason, We developed General Agriculture Mobile Service Platform. The Platform is designed to make these information services fit to be accessed by mobile users, and to make those mobile phone users can use these services without Internet connection. To achieve that, a descriptive language is designed to describe the services’ inputs and outputs, used to passing requests and responses between the platform and the mobile client software. With those descriptions, client software can generate user interface on the client mobile device. Using that interface, user can manipulate service. The communication between client side and the platform can be carried by SMS, MMS as well as TCP, so that the devices which don’t have Internet connection can access those services. Keywords: Mobile service, Mobile platform, SMS protocol, MMS protocol, Mobile protocol.

1 Introduction Do not have internet connection, one can only be reached by mobile specific communication protocol. Most of the users of cheap mobile devices, as their devices has so limited operability and is not fit for using internet application, will simply chose to have none internet connection. Their mobile device supports only phone call and SMS. To make our service being reachable to these users, a SMS/MMS based communication protocol is developed to handle communication via SMS and MMS, and an independent mobile communication protocol layer is added to the platform. With SMS based communication, the platform can be reached by 100% of mobile users. The processing capacity, presentability, and operability of mobile devices are limited, complicate data presentation and complicate operation can not be done on mobile device. Standard web based user interface is not fit for mobile users. Between mobile *



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H. Hu and X. Su

devices, capacity of display and operate vary so greatly that can not design a standard client user interface to fit all types of devices. On mobile devices, software installation and running is limited, complicate software functions is not applicable. Client software must adaptable to all these limitation.

2 Platform Design 2.1 Client/Server Architecture Mobile terminal may not support Internet connection. Client/server is the only applicable architecture to leave room for the special designed mobile communication protocol. 2.2 Mobile Protocol Layer Seal mobile communication protocol to an independent layer, simplified both the client and the server software design, and made maintenance and upgrading easy. 2.3 Web Service Composition Server composes available web services into functions which fit for mobile user. The client software can only have a set of very limited functions, so that it can not call complicate services by itself, services must be packaged by server firstly, to make them fit for calling by client software. 2.4 User Interface Auto Generation Based on the capacity of the current mobile device, client software generates user interfaces for each service according to its service description. Each function offered has a formal description, which includes descriptions to all parameters that function needs. All functions consists the service list, which is the data source of service choice list on the client side. With those descriptions, client software can generate user interface to launch calling to those functions, and to handle the returned data. 2.5 Command Pattern Client side software launch calling to server side functions by translation user inputs into commands and sending them to server. The server translates each command to the actual calling to launch the corresponding function, and then package the returned data as response to that command. Then client side handles the returned response to generate user interface to display it. 2.6 Information Leaning Based on Personal Knowledge and Demand Server traces each user’s access records, calculates his possible knowledge structure and interest point. With that information, server can lean the information returned by

A General Agriculture Mobile Service Platform

43

remove those out of the scope of user’s knowledge and those out of user’s interest. This function can be switched off manually.

3 Implementations 3.1 SMS and MMS Based Communication Protocol A single SMS message can carry only 140 byte information, equal to 140 ASCII characters or 70 double byte symbols. This sets the up limit of a single data package. Such a package size is too small to carry information (compared to other communication protocols, such as Ethernet’s 1544 byte package). To transmit data over such a tiny package, the first thing must do is to make the package structure as simple as possible to leave room for data; the second thing must do is to make data can be carried by multiple packages, that means data must be dissembled at transmit end and assembled at receive

Fig. 1. A typical session sequence

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H. Hu and X. Su

end, some kind of sequence control must be introduced to assure recover data in original sequence [1.2]. There are 3 tasks of the protocol: z

establish session and offers the current endpoint context to server

z

submit service request and receive returned data

z

end session

Figure 1 shows a typical session, includes session starting and ending, and some service requests/responses. Corresponding to these 3 tasks, 2 types of packages are needed, one is used to establish and end sessions, the other is used to carry service requests and responses. Every package is a SMS message, to keep the session traceable and according the sequence of sending and receiving, each package has a serial number. Because the system also allow user edit and send SMS message manually, and those manually edited message should not be expected to have correct serial number like the automatic generated ones, so if a package do not have serial number, it will be handled as is, without premising in correct sequence. The packages with no serial number can not be used to establish or end session, update contexts, choose service to entry, or to do all other platform specific job, but can be used to carry service requests, whether it is acceptable is leaved to the service it requests. Serial number should always stay at the beginning 4 character, starts with ‘#’, followed by 3 digits, that means the largest serial number is 999. Statistically, most of user sessions won’t use so many packages to make serial number overflow. If overflow really occurs, it still doesn’t matter, serial number simply start from 0 again, we simply regards serial 000 is larger than 999, there will be almost zero chance to disturb communication sequence. If a package is sent more than once (often caused by mobile network provider), the duplicated ones will have the same sequence number, if received more than 1 packages which have the same serial number, the package latest received will be kept and the earlier ones will be simply discarded. Unlike most communication protocols, there is no ACK package, the reliability of communication is assured by SMS itself. If ACK is really needed, ACK words can be put directly after the serial number, each ACK word starts with the prefix ‘R’, followed by 3 digits represents the replied package’s serial number, nothing should be put between each pair of neighboring ACK words and between serial number and ACK words. A package with ACK words may look like this (Figure 2): 1__________________11__________________21___________________31_______________40 #nnnRnnnRn

nnRnnnXXXX

XXXXXXXXXX

XXXXXXXXXX

Fig. 2. The response package structure

Here ‘n’ represent digit, as well as ‘X’ represents data. The package’s length is not fixed. And no method is designed to check its length, assurance of the integrity of package is leave to SMS.


45

3.2 Voice Service The mobile communication protocol is not fit for transmit long text, and the mobile device itself is often not fit for read much text. If large text needed to be delivered to user, voice is a reasonable choice. Along with SMS, voice is the only other communication method which all mobile device must supports. Regarding that voice can not be formalized, complicate interaction can not be carried by voice service, the only job fit for voice is deliver text. The voice service offered by platform is quite simple. Every user by default will own a voice box, at most 9 voice messages can stored in it, each of them has a title and content. If the box is already full, coming of new message will overwrites the oldest one without warning or confirmation. Client software offers a user interface to manage user’s voice box, user can see message title list and delete message through this interface, but can not get message contents. To get contents, user must call the number of voice service, and then voice service will read out each message title, starts with a serial number, user push the digit button of the serial number will make voice service read the message with that number to user. 3.3 Client Software Developed with J2ME Simply say, client software’s job is to translate user’s operations into calling to services, server finish those calling and return data to client, then client generate an user interface to display the returned data as while as to prepare user’s further operation which using some of those data as input. The first, client must be able to communicate with server, so it must have mobile communication protocol layer, client software can detect mobile device’s capability of communication automatically, and decide the most suitable communication method, user can also manually set communication method. Client software must very flexible, and can adapt to all kinds of limits of all different mobile devices. All user interfaces can adjust automatically to fit for the device’s capacity. Most of the user interfaces are generated automatically, so that services can be added dynamically, without updating the client software. There are some basic operation interfaces which are not automatically generated, they are client software configuration screen, user profile management screen, voice box management screen, service selection screen. After entering a service, all user interfaces are generated based on the service description and function list of the service. To minimize communication, service description is not strictly formalized, the grammar is very simply, many defaults are automatically applied while no declaration presents. For example, the only allowed data type is number and string, if a piece of data comply with any digital format, it will be regard as digits, otherwise it will be look as string, so that the extra data for data type is not needed[3]. 3.4 The 3 Layered Server End Software Server end is consisted by 3 layers, mobile protocol layer, basic platform service layer, and service composition layer.

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At server side there is a counterpart mobile protocol layer handling communications via mobile protocol, but the implementation of this layer is not the same as client side. Unlike the client, server is not naturally support mobile communications; it must connect to some mobile capable devices to extend its mobile capacity. And then, the protocol layer divided into 2 parts, running on the server and its mobile extension devises respectively. Basic platform service provides basic functions such as user profile management, user activity tracing, personalized sorting and filtering, voice box management, and service management. The core function of the platform is to manage services, these services are described in OWL-S documents, and ready to be called by the client side. Each service has a name and a description. User can regard each service as an entry point to access a certain kind of resources, by calling that service, user can access the resources the service provides. A simplest service can only provide a resource list and detail data of each resource. In more complicate occasion, category and search are provided to assist user to locate resource mo efficiently. If resources can be created, changed or removed by user, the service should also include CRUD functions. Beside these, a service should not include any unnecessary function. This limit of simplicity on service simplified user interface generation. If a service originally provides more than this, it should be simplified first, before added to platform [4]. The platform provides a general propose filtering and sorting function based on user’s personal knowledge structure and current interest point. An OWL document is maintained for each user to records the knowledge the user may have. User’s knowledge is inferred from user’s activities, so do to user’s current interest point. Personalization is implemented by another independent software package, and further discussion on personalization is beyond the scope of this paper. What make sense to the platform is that personalization helps significantly reducing the communicated information, and interactive rounds. 3.5 Web Service Composition with OWL-S The server does not care about where the OWL-S description comes from, and whether or not it is correct or effective, those jobs are handled by other systems. The server just use these OWL-S descriptions, call web services according to them, if something failed, a standard error message will be shown. Services is added into platform through service management user interface, this is a web based UI and can not be accessed by common users. Each service has a group of processes, OWL-S describes how to launch these processes. At client side, while entering a service, there is a main menu shown to user, lists all the processes the service had. By select the menu item, client side sends command which at server side launches the corresponding process. At every step of the process triggered, if some parameter is not available from environment and previous outputs, that means user input is needed, at that time, a user interface to input those data is generated. Thanks to the limit of simplicity, all these processes are simple, only sequence flow can appear. So that the


47

platform need to support only sequence control flow, and can regardless loops or branches the OWL-S processes may have [5,6].

4 Conclusions By introducing mobile communication protocol, make the platform can cover 100% of mobile users. By introducing web service composition, make it easier to enrich service contents available to mobile users. All these will attract more mobile users and make the mobile technology valuable to agriculture production.

Acknowledgement The research was supported by the national 863 project, Mobile intelligence service for agricultural scientific & technical information (project code 2007AA10Z236) and special fund of basic commonweal research institute project of information institute of CAAS.

References [1] [2] [3] [4] [5] [6]

Ortiz, E.: The MIDP 2.0 Push Registry (January 2010), http://blog.csdn.net/memhoo/archive/2008/03/02/2139611.aspx JSR 120 Expert Group: Wireless Messaging API (WMA), JSR 120, JSR 205. SUN corporation Mahmoud, Q.: Getting Started With the MIDP 2.0 Game API. [EB/OL] (September 2005), http://developers.sun.com/mobility/midp/articles/gameapi Richardson, L., Ruby, S.: RESTful Web Services. O’Reilly Media Inc., Sebastopol (2006) Saadati, S., Denker, G.: An OWL-S Editor Tutorial. [EB/OL] (May 2010), http://owlseditor.semwebcentral.org/documents/tutorial.pdf W3C Member Submission: OWL-S: Semantic Markup for Web Services [EB/OL] (September 2004), http://www.w3.org/Submission/2004/SUBM-OWL-S-20041122/

A Halal and Quality Attributes Driven Animal Products Formal Producing System Based on HQESPNM Qiang Han∗ and Wenxing Bao School of Computer Science and Engineering, BeiFang University of Nationalities, Yinchuan Ningxia, P.R. China 750021 [email protected]

Abstract. Usually, halal animal products formal producing system consists of several components that cover major stages including Pre-processing, Processing and Post-processing. In this paper, we present five information systems to implement fundamental functions of formal management, scientific foods management, animal epidemic disease diagnose and prevention, processing standardization and market management, which respectively map to those components mentioned above. As halal animal products formal producing system, there are Halal & Quality attributes existing in all of the five information systems. Thereby, concentrated and systematic controlling of Halal & Quality attributes could improve whole quality of the producing system and ensure products is halal. Addressed to the problem of controlling scheme, first, this paper given a Halal & Quality Elements Extended SPN Model (HQESPNM) in detail. Second, it propose Platform-Independence architecture of the formal producing system based on HQESPNM through infrastructure of database integrate middleware. Finally, this paper given an Electronic-Agriculture Services case through Platform-Specified Software based on SOA to certificate that the model proposed by this paper is feasible for halal animal products system. Keywords: Halal, Quality, Traceability, Petri Nets, HQESPNM.

1 Introduction At the beginning of 80’s of last century, based on the Petri Net [1], Molly presented Stochastic Petri Net through associating a stochastic delay time to every transition from ready-to-fire to firing [2]. With the development of science, computation science based on high-performance computing becomes more and more important[5].As the Stochastic Petri Net presented, it was used in many applications of modeling, analysis and efficiency test, such as communication protocols, workflow design etc. However, Stochastic Petri Net is not suitable to each application aspect completely. For example, to the formal producing and quality certification of Halal Animal, except general elements of normal management are suitable to Stochastic Petri Net for information system modeling and computation, the type of its Halal and Quality elements affection to all of steps in whole management process is through controlling the ∗



A Halal and Quality Attributes Driven Animal Products Formal Producing System

49

process and not by any time-element or stochastic-element. Once the Halal and Quality elements are destroyed in the management process, the products have not any meaning for Moslem people and other people. Including all the animal product producing process of Pre-processing, Processing and Post-processing, in breeding, propagate, feed, fattening, slaughtering, transportation and market circulating, how to guarantee the products are processed under an consistency environment according to Halal and Quality standard, is the key of information system which service for this aspect. To solve the problem above, this paper proposed an approach through combination technique [13] of Place expanding Petri nets [6] and Stochastic Petri Net [2], and by the approach, this paper given a Halal & Quality Elements Extended SPN Model (HQESPNM) in detail. The primary thought of the approach is separate the problem of the aspect into two part, the part about general performance computation can be solved by Stochastic Petri Net, and the other part about Halal & Quality elements can be solved by Place expanding Petri nets. So that, by this means, through the function of the two Petri net theories, the problem of the aspect can be solved successfully. In the detailed scheme of the halal animal products formal producing system model based on HQESPNM, this paper mainly given design of the model which separate Halal & Quality attributes into independent subsystem, and the implementation of its algorithm. The organization of the remainder of this paper is as follows. Section 2 given the basic concepts and notations, and system model is given in section 3. Section 4 discussed how to use the system to solve the problem through an example. Finally, section 5 summarized the main results and points out the future work. The word “iff” means “if and only if” in this paper.

2 Basic Concepts and Notations The concepts and notations of Petri nets are derived from some documents [7-12]. 2.1 Stochastic Petri Net Definition 1. A Petri net is a four-tuple

∑ = (P, T , F ; M ) such that: 0

(1) P is a finite set of places, and T is a finite set of transitions, and

PI T = φ , P U T =/ φ ; (2) F ⊆ (P × T ) U (T × P ) is a set of arcs; (3) M 0 : P → {0,1,2,...} is the initial marking. Generally, ∀x ∈ P U T , the pre-

x = {y | y ∈ P U T and (y, x ) ∈ F } , and the post-set of x is x ={y | y∈PUT and (x,y)∈F} and . x U x . is the spanning-set of x. If P = P, T = T and F ∈ ((P × T ) U (T × P )) U F , ∑1 = (P1 , T1 , F1 ) is called the

set of x is

.

.

1

1

1

spanning subnet of P1.

1

1

1

1

50

Q. Han and W. Bao

Definition 2. Let M(p) be the number of tokens in place p. For

t ∈T ,

. (1) t is enable under the marking M, denoted by M [t >, iff∀p∈ t : M ( p ) ≥ 1; ;

(2) If t is enable under the marking M, then t can be fired. The marking ' M ' is obtained from M by firing t, denoted by M [t > M :

⎧ M ( p ) − 1 , ( p , t ) ∈ F ∧ ( t , p )∉ F ⎪ M ( p ) = ⎨ M ( p ) + 1 , ( p , t )∉ F ∧ ( t , p )∈ F ⎪⎩ M ( p ), otherwise '

Thus, M′ is reachable from M. The set of reachable markings from M is denoted as R(M). The properties derived from execution of the Petri net are called dynamic properties or behavioral properties. A Petri net ∑ = ( P, T , F ; M 0 ) is called safe iff ∀M ∈ R( M 0 ), ∀p ∈ P, M ( p) ≤ 1 is satisfied. Definition 3. For a Petri net

∑ = ( P, T , F ; M

0

),

M ∈ R ( M 0 ), if M ∈ R ( M ) for ∀M ∈ R ( M ), then M is called a home state. '

'

∑ is a

reversible net system if M 0 is a home state. Definition 4.

∑ = ( P, T , F ; M

0

) is a Petri net.

∑

is said to be:

(1) weakly live iff ∀M ∈ R ( M 0 ), ∃t ∈ T such that M[t > .

(2) live iff ∀M ∈ R ( M 0 ), ∃t ∈ T , ∃σ ∈ T such that M [σ > M [t > . *

Definition 5.

∑ = ( P, T , F ; M

0

'

, K , W ) is a Place/Transition Net, where:

(1) ( P, T , F ; M 0 ) is a Petri net.

(2) K : S → N + U {∞} is Place Capacity function. (3) W : F → N + is Arc Priority function. (4) ∀p ∈ P : M 0 ( p ) ≤ K ( p ) .

∑ = ( P, T , F ; M 0 , K ,W , λ ) is a continuous-timed Stochastic Petri Nets, where: (1) ( P, T , F ; M 0 , K ,W ) is a Place/Transition Net. (2) λ = (λ1 , λ2 , λ3 ,L, λm ) is set of transition average fired rate. λi is transition aver-

Definition 6.

age fired rate of t i ∈ T , which represent the fired times of ti in an unit time. 2.2 Place Expanding Petri Net (PePN)

Based on the introduction of Petri Nets and Stochastic Petri Nets above, we can calculate performance data through modeling system. However, in the actual application of


51

it, because of the dynamic change properties of modeling objects, the capacity of description for them is limited. So some researchers presented Place expanding Petri Net Models, example Place expanding Petri Net[6].Its definition is: Definition 7.

∑ = ( P, T , F ; M

0

) is a Place expanding Petri Net(PePN) , where:

(1) S={s|s is Place expanding Petri Net or s ⊆ S , S is place sets of '

'

(2) ∀( x, y) ∈ F : x ∈ S ∧ y ∈T ⇒ ∃z ∈ S, ( y, z) ∈ F .

∑

.

(3) ∀( x, y) ∈ F : x ∈T ∧ y ∈ S ⇒ ∃z ∈T , (z, x) ∈ F . 2.3 Halal and Quality Elements Extended SPN Model (HQESPNM)

Based on the Stochastic Petri Nets and Place expanding Petri Net, this paper presented a Halal&Quality Elements Extended SPN Model (HQESPNM). Definition 8.

∑ = ( Nh, Nq, Np, F ) is a HQESPNM, where:

(1) Nh = ( PNh , TNh , FNh ; M 0 ) is a Petri Nets. Nh

(2)

Nq = ( PNq , TNq , FNq ; M 0Nq ) is a Petri Nets.

(3)

Np = ( PNp , TNp , FNp ; M 0Np , K Np ,WNp , λNp ) is a Stochastic Petri Nets.

(4) F = {< p, t > ∪ < t , p >| p ∈ ( PNh ∪ PNq ), t ∈ TNp } < p, t > means a Halal or Quality data flow/transition from Nh or Nq to Np ; on the contrary, < t, p > means a Halal or Quality data flow/transition from Np to Nh or Nq . For the application in the Introduction of this paper, Nh represent abstract of Halal-element relation, Nq represent abstract of Quality-element relation, Np represent abstract of Animal Products Producing, F represent abstract of controlling from

Nh and Nq to Np and reflection from Np to Nh and Nq . Based on the HQESPNM, we can find that the correctly systematically running of Np has to be controlled under Nh and Nq through F . Then this paper given the system architecture and algorithm to implement HQESPNM as follow.

3 System Model In this section, we gave a Meta-model of Compute-Independence (CIM) for HQESPNM firstly. For the system design, according to the CIM, a Model of Platform-Independence (PIM) for HQESPNM was given through approach of OOA&OOD [3][4]. In the PIM, we illustrated the relation between Nh and Np as well as the relation between Nq and Np .

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3.1 System Architecture

The basic components of halal products formal producing system includes: (1) Formal Management Information System (FMIS). (2) Processing Standardization Information System (PSIS). (3) Market Management Information System (MMIS). (4) Scientific Food Management Information System. (5) Animal Epidemic Diagnose Information System. (6) Quality Traceability Information System (QTIS). (7) Halal Traceability Information System (HTIS) (8) Public Data Traceability Information System. (9) Traceability Data Bus based on Data Integrity Middleware. Among the components above: (1),(2) and (3) consists of the Np in HQESPNM, which is the major body of halal products formal producing system, representing Pre-processing, Processing and Postprocessing stage respectively. (4) and (5) assist the normal running of Np . (4) is a non-independent component of the Formal Management Information System, on the contrary, (5) is an independent component based on artificial intelligence, which can be loaded or unloaded freely. (6) and (7) consists of the Nq and Nh respectively in HQESPNM, which are the kernel of halal products formal producing system. (8) is a trusted third-party component, which provide common qualification data to the Nq via exchange interface of (9). (9) is Traceability Data Bus based on Data Integrity Middleware, connecting the components mentioned above as a whole system, assurance the Np could be operated under the controlling of Nq . CIM of system architecture can be described as Fig.1.

gˉ

st-

Traceability Data Bus

Po gˉ

sin

sin

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es

oc oc

Pr

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Quality Halal Traceability Traceability System System

6FLHQWLILF )RRG 0DQDJHPHQW ,QIRUPDWLRQ 6\VWHP

Data Integrity Middleware 3XEOLF'DWD 7UDFHDELOLW\ 6\VWHP

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Fig. 1. CIM of System Architecture


3.2 Design of

53

F in HQESPNM

According to the CIM of System Architecture above, next, through the theory of Stochastic Petri Nets and UML, we can map components and complex connectors into package, function specifications into interfaces, entry points into abstract class, and inner specification of component into comments respectively. Then, according to F in the definition 8, we can give the kernel PIM of System Architecture concentrated in relation between Nh , Nq and Np as Fig.2, which reflects the F in HQESPNM.

47,6

+7,6

)0,6

P Nh

T Np

36,6

T Np P Nq P Nh T Np

00,6

T Np P Nq

PNp

T Np

Fig. 2. F in HQESPNM

4 Overview of HQESPNM by Example Based on the research above, we developed a set of software prototype named as Hqespnm1.0 to certificate the correctness of the HQESPNM. In this section, we presented an example to introduce the set of software prototype as Fig.3. Hqespnm1.0 consists of Halal Traceability Information System (HTIS) and Quality Traceability Information System (QTIS), which show as upper part and Lower half of Fig.3 respectively.

Fig. 3. Hqespnm1.0 based on HQESPNM

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Q. Han and W. Bao

Fig. 3. (continued)

5 Conclusion and Future Working Address to Halal & Quality attributes assurance problem of formal producing system, the basic concept and notation of HQESPNM is given firstly, and the software model including architecture and design is presented secondly. Based on the model, this paper introduced an application example in electronic agriculture domain covering whole producing process including Pre-producing, Producing, Post-producing finally. Results indicated that the HQESPNM separate the normal producing elements and Halal & Quality controlling elements into Petri Nets and Stochastic Petri Nets through Place expanding Petri Net [6] successfully. In future, the formal work of HQESPNM should be implemented to certificate its correctness.

Acknowledgement This paper is supported by National Key Technology R&D Program of China under Grant No.2007BAD33B03, Natural Science Foundation of NingXia Province under Grant No.NZ0955 and the Colleges Oriented Scientific Research Fund of NingXia Provincial Education Department of China under Grant No.2008JY009. Additionally, we should thank for the software prototype development work distributed by our colleagues and master candidate students: Ding HongSheng, Yang YongSheng, Shi Liang and Liu Yang.

References 1. Petri, C.A.: Kommunkation mit automaten. Schriften des IIM, vol. 3. Institut fur Lnstrum Entelle Mathematik, Bonn (1962) 2. Molly, M.K.: Discrete time stochastic Petri nets. IEEE Trans. Software Eng. SE-11(4), 417–423 (1985) 3. Shao, W.-z., Yang, F.-q.: Object-Oriented System Anysis. Publishing House of Tsinghua University (2006)


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4. Shao, W.-z., Yang, F.-q.: Object-Oriented System Design. Publishing House of Tsinghua University (2007) 5. Cui, H.-q., Wu, Z.-h.: MPI Programs’ Petri Net Model and Its Dynamic Properties. Journal of System Simulation 18(9), 2455–2460 (2006) 6. Qi, F.-m., Yu, B., Shi, L.-j., Mou, L.-k.: A Modeling Method of Software Project Management Based on Petri Nets. Journal of System Simulation 19(suppl. 1), 75–78 (2007) 7. Peterson, J.L.: The Theory of Petri Net and System Simulation. Wu Zhehui (Trans). Publishing House of China University of Mining Technology, Xuzhou (1989) 8. Murata, T.: Petri Nets: Properties,Analysis and Applications. Proceedings of the IEEE(S0018-9219) 77(4), 541–580 (1989) 9. Yuan, C.: The Principles of Petrinet. Publishing House of Electronics Industry, Beijing (2005) 10. Lin, C.: Stochastic Petri Nets and System Performance Evaluation. Publishing House of Tingshua University, Beijing (2005) 11. Zhan, H., Gu, J.,: Study of the Normal Generalized Stochastic Petri nets and its Application in Testing System. In: IEEE Instrumentation and Measurement Technology Conference Proceedings, pp. 1123–1128 (2006) 12. Renato Vazquez, C., Recalde, L., Silva, M.: Stochastic Continuous-State Approximation of Markovian Petri Net Systems. In: Proceedings of the 47th IEEE Conference on Decision and Control, pp. 901–906 (2008) 13. Han, Q., Ding, J., Bao, W.: IEEE Proceedings of the 2009 International Conference on Computer and Computing Technology Applications in Agriculture (2009)

A Metadata Based Agricultural Universal Scientific and Technical Information Fusion and Service Framework Cui Yunpeng1, Liu Shihong1, Sun SuFen2, Zhang Junfeng2, and Zheng Huaiguo2 1

Key Laboratory of Digital Agricultural Early-warning Technology, Ministry of Agriculture, Beijing, The People’s Republic of China 100081 2 Agriculture Sci-Tec information institute of Beijing Academy of Agriculture and Forestry Science, Beijing, The People’s Republic of China 100097

Abstract. The paper introduced a metadata based Agricultural scientific and technical Information fusion and Services framework. Through Agricultural scientific and technical Information dataset core metadata and Services core metadata, the distributed and platform-independent Information fusion can be implemented, based on the information fused resources in base layer of the framework, many applications can be developed, such as mobile communication based mobile Information service, voice text converter based voice information service, smart Q & A application etc., and the solution is an available and effective solution for the fusion and services of agricultural scientific and technical information resources, because the solution can integrate the data with different format from different data sources, so the solution can be used to construct the data layer of agricultural scientific and technical universal information services. Keywords: Information fusion, Metadata, Agricultural scientific and technical Information service.

1 Introduction The construction of Agricultural informatization in China has made rapid progress in recent years. There are now over 2,000 E-commerce web sites, more than 6,000 Agricultural web sites in China, and a large amount of application and digital products have emerged[1], the agriculture productivity in China has been enhanced significantly. But on the other hand, some problems also emerged during the period. because there are too many information resources located in different data sources, and a lot of these information are duplicated, it’s very difficult for users to find the right knowledge and information they really want, and users can’t extract useful knowledge from just one or several information. The crux of all these problems is information fusion, based on information fusion, the information resources with different formats from different data sources can be integrated in one logical whole, the retrieval to this logical whole is just like retrieval from one dataset, the duplicated information entities can be removed automatically, based on the fusion of the information resources, many D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 56 – 61, 2011. © IFIP International Federation for Information Processing 2011

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applications can be developed, such as mobile service, intelligent Q&A system, the cross- database retrieval application etc. The most effective scheme for information fusion nowadays is metadata, it is the core of information fusion, and now there are already some successful case which use metadata based framework in information fusion, such as the scientific database service platform of CAS, the agricultural scientific data sharing platform of CAAS, etc., this paper will discuss a metadata based agricultural scientific and technical information fusion and service framework as well as the standards worked out for the framework.

2 Metadata and Metadata Standards The definition of metadata is: metadata is the data about data[2]. In fact, metadata is a set of data that can describe and identify a specific information entity, and help users find and achieve related information resources object. There are two kinds of metadata standards, core metadata standard and expanded metadata standard, normally, expanded metadata standard was expanded from core metadata standard and used in some specific area. The usage of metadata standards include: 1. Information management. Metadata can describe information entity, that means all the information entities can be “labeled” with metadata, through metadata, the management of information entities can be promoted. 2. Information discovery. With metadata, the retrieval of information entities is in fact the retrieval of metadata registration information, so the metadata registration information can be regarded as the sources of information discovery[3]. 3. Information acquisition. Normally, the position and the type Information is included in metadata, so users can acquire information entities very easy, thus users can utilize information more effectively. 4. The integration and sharing of information resources. All the information entities is registered with universal or compliant metadata standards, so the integration and sharing of information resources turns into reality[4].

3 Metadata and Information Fusion Figure 1 shows the principle of information fusion framework with metadata. There are 3 layers in the framework. at the bottom of the framework is data sources layer, where contains different type, different format information resources from different sources distribute anywhere, such as databases, multimedia resources, literatures, scientific data, Internet resources etc.. The middle layer is services metadata fusion layer, in this layer, all the data sources at the bottom layer are identified by services metadata, this layer include a services metadata value database, where all the data sources information saved, users can locate a data sources through these services metadata values and

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connect to a data source to get the information they need. The top layer of the framework is dataset metadata fusion layer, all the datasets locate in the data sources at the bottom layer will be identified by dataset layer, so there is a large dataset metadata value databases in this layer, users can retrieve the information entities in the datasets through retrieving the dataset metadata values.

Fig. 1. The framework of information fusion with metadata

If a user want to retrieve an information entity, he/she enter keywords into an application that provides the services above, the system will first retrieve the information in the dataset metadata values database, find which dataset include the information entities match the keywords the user entered, and then locate the data sources where the service metadata described, find the connection information of the data source, then connect to the data source, send query information, get the results, and feedback results to the user.

4 The Agriculture Information Resources Dataset Core Metadata The agriculture information resources dataset core metadata is a metadata standard which regards agriculture information resources as descriptive objects, it is expanded from the scientific database core metadata standard of Chinese academy of science. It defines a set of data modules and elements. The main body of the standard includes 6 required modules: dataset description information, data quality information, dataset distribution information, metadata reference information, services reference


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information, and structure description information, and 2 assistant modules: range information and contact information. The assistant modules can only be quote by the elements of the required modules, and cannot be used separately, See Figure 2. C

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All the elements in the standard has 9 attributes, as shown in Table 1. Table 1. The attributes of the elements of agriculture information resources dataset core metadata Name of attribute Chinese name English name Identification Definition Type Range Optional Maximun appearance Note

Description Chinese name of the element English name of the element The unique identification of the element, string. The specifications description of the meaning of the element. The type of the element, the available types include: composite(the element contains sub elements),integer, float, text, date, time, datetime etc. The allowed range of the value of the element The element is required or optional The maximum appearance of the element, such as 1 (only once) Supplementary specifications of the element

、

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5 Agriculture Information Resources Services Metadata The agriculture information resources services metadata defines specific services metadata specification. For a specific service, the metadata specification is relative fixed, so we can find a model to define any services. Figure 3 shows the universal model of agriculture information resources services metadata. The universal model includes 5 elements: service type, service name, service URI, service description and parameters, the parameters can be one or more, each parameter has parameter name and parameter value.

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For example, the dataset connection service metadata is like the following (see Figure 4 ). NameofDatasetconnectionservice

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From the example, we can see that all the information needed to connect to a database are contained in the metadata values. That is, an application can connect to the database automatically with these information, once an agriculture database is indexed with agriculture information resources dataset connection service metadata, the application can connect to the database and get results from database, the whole process will be finished automatically.

6 Conclusion Information fusion is very important for agriculture scientific and technical information services, but it’s very difficult to find an effective mechanism to implement real agriculture information fusion. Metadata provides available means to integrate different type, different format information resources from different sources, and all the information sources can be integrated into one logical entirety. Based on the integrated information resources, different application can be developed, such as mobile communication based mobile Information service, voice text converter based voice information service, smart Q & A application etc., so the universal information services can be implemented, thus, the quality of agriculture information services will be promoted greatly.

Acknowledgement The research was supported by the special project from ministry of agriculture of the people’s republic of China, named study of agriculture informatization standards system and special fund of basic commonweal research institute project of information institute of CAAS, and National 11th five-year technology based plan topic named study of Agricultural product quantity Safety Data obtained standards (2009BADA9B02).

References [1] [2] [3] [4]

Zheng, H., Tan, C.: The Integration and sharing of Agricultural Information Resources in network environment. Journal of Anhui Agriculture Science 36(13), 5665–5668 (2008) Xiao, L., Chen, L.: Chinese Metadata Standard Framework and Its Applications. Journal of Academic Libraries 19(5), 29–35 (2001) Jenning, M., Marco, D.: Universal Meta Data Models. Wiley Publishing, Inc., Chichester (2004) Qian, P., Su, X., Cui, Y.: Study on agricultural scientific and technical information core metadata. Agriculture Network Information (2), 18–21 (2006)

A Method to Calibrate the Electromagnetic Tracking Instrument When Measuring Branches of Fruit Trees Ding-Feng Wu, Jian Wang, Guo-Min Zhou, and Li-Bo Liu Agricultural information institute of CAAS, Beijing 100081, China [email protected]

Abstract. To reduce the effect from instrument error when getting characteristic parameters of branches of fruit trees by the electromagnetic tracking instrument, a calibration method was sounded based on a discussion of the instrument error of electromagnetic tracking instrument. Finally, the method was tested in an experiment. By comparing the data of the experiment and the standard data which was got by slide caliper, we proved that the method is effective in increasing the accuracy of measurement. Keywords: Fastrak; Instrument error; Calibration.

1 Introduction China is the biggest producer of fruits in the world. In many parts of this country, the fruit industry has become the pillar industry. based on the measurement of fruit tree structure, the research of the connection between the structure and the output, the utility rate of luminous energy and the anti-disease ability of a fruit tree is an important impetus of developing of punning skill and breeding technique of fruit trees [4]. Electromagnetic tracking instrument is a kind of digital measuring tools based on electromagnetism [3]. It is an effective tool of getting structure data of fruit trees because it is not only an easy-to-use, extremely accurate and broad action sphere device but also a powerful survey tool which can track the space track and calculate the inclination angle of stylus [2]. The electromagnetic tracking instrument is vulnerable to external magnetic effects. It will fall in complicated electromagnetic environment [1]. Besides, after a long time working, the status of equipment will be different from the initial status and the accuracy of the device will reduce. When measuring branches of fruit trees, a high degree of accuracy is required, so the electromagnetic tracking instrument must be calibrated before working [4]. In this paper, a calibration method is put forward.

2 Materials and Methods 2.1 Device Fastrak is an advanced electromagnetic tracking instrument [1]. It was used as the measuring device in the experiment. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 62–67, 2011. © IFIP International Federation for Information Processing 2011

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Assume the error of the measuring instrument to be calibrated is μ and the error of the standard measuring device is μ’. Then in the course of the instrument calibration, we must ensure thatμ’ is at a lower order of magnitude than μ, otherwise the calibration may increase the error because the error of standard device affects the result. As we known, the Fastrak electromagnetic tracking device can working with accuracy of 0.8mm [1], which means the normal rulers can not provide a standard Reference Data, so we use a slide caliper with the precision of 0.05mm as the standard measuring device in the experiment. 2.2 Analysis of Error Because of the effects of devices and experiment environment, the measurement result of physical amount is definitely different with the real value, the difference is called measurement error, the part which caused by the imperfect instrument structure and the external environment is named instrument error. When Fastrak is working, following causes may bring instrument error: 1. External magnetic effects 2. Deviation of origin of coordinate 3. Instrument mechanical wear and decline of circuit state 2.3 Error under the Magnetic Effects In the electromagnetic environment, eight space points were measured by Fastrak electromagnetic tracking instrument, every point was measured ten times. The result was compared with the standard data got by slide caliper. Error of one point’s ten times measurement is shown in Figure 1. 100 50 Exp No

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Fig. 1. Randomness of electromagnetic tracking instrument’s error in electromagnetic environment

As shown in Figure 1, in electromagnetic environment, Fastrak’s error is random and irregular. So the error brought by external magnetic effects is hard to reduce by calibration, the calibration should be conducted without the effect of external magnetic.

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2.4 Calibration Method Assume the initial space coordinates of origin are (x, y, z), after the deviation, the coordinates are changed to (x’, y’, z’), the amounts of deviation are Δx, Δy and Δz, so Δx= x’ - x, Δy= y’ - y, Δz= z’ – z. When a space point is measured by the measuring device, assume the coordinates of the point got by measuring device are (X, Y, Z), then the real coordinates are (X’-Δx, Y’-Δy, Z’-Δz). As we known, Δx, Δy and Δz are constants, so we just need to use the above method to n space points to get their Δxi,

∈

Δyi, Δzi (i [1,n]), Δx, Δy and Δz can be calculated by following formulas: n

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Figure 2, when L was enough small, the time of signal transmitting was too short to be accurately measured by the device, as a result, it brought in an un-negligible error, so the calibration can only reduce the constant error. The calibration method is shown in Figure 3.

Fig. 3. Flow chart of Calibration method

2.5 Experiment We got one space point in each quadrant of the eight quadrants conformed by the Spatial three dimensional coordinate axis and one space point on each axis, so we had eleven points which were measured in the experiment. Those points were measured by Fastrak electromagnetic tracking instrument. The result of the measurement was processed by the above calibration method. At the end of the experiment, we compared the result with the standard data got by slide caliper.

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3 Result and Analysis The experiment result is shown in Figure 4, Figure 5 and Figure 6. 4 2 0 -2

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It is clear that the errors after the calibration were less than the errors before the calibration. It proved that the method does work.

4 Conclusion and Discussion Based on the analysis of causes and characters of the error of the electromagnetic tracking instrument, a calibration method was given, and then an experiment proved the availability of the method. The method can improve the accuracy when measuring branches of fruit trees by the electromagnetic tracking instrument.


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References 1. 2. 3. 4.

Polhemus: 3Space Fastrak Users Manual (2000) Ivanov, N., Boissard, P., Chapron, M., Valery, P.: Estimation of the Height and Angles of Orientation of the Upper Leaves in the Maize Canopy Using Sterovision. Agrononie (1994) Danjon, F., Sinoquet, H., Godin, C., et al.: Characterisation of structural tree root architecture using 3D digitising and AMAP mod software. Plant and soil (1999) Thanisawanyangkura, S., Sinoquet, H., River, P., et al.: Leaf orientation and sunlit leaf area distribution in cotton. Agricultural and Forest Meteorology (1997)

A Method of Deduplication for Data Remote Backup Jingyu Liu1,2, Yu-an Tan1, Yuanzhang Li1, Xuelan Zhang1, and Zexiang Zhou3 1

School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, P.R. China 2 School of Computer Science and Engineering, Hebei University of Technology, Tianjin, 300010, P.R. China 3 Toyou Feiji Electronics CO., LTD, Beijing, 100081, P.R. China [email protected], [email protected]

Abstract. The paper describes the Remote Data Disaster Recovery System using Hash to identify and avoid sending duplicate data blocks between the Primary Node and the Secondary Node, thereby, to reduce the data replication network bandwidth, decrease overhead and improve network efficiency. On both nodes, some extra storage spaces (the Hash Repositories) besides data disks are used to record the Hash for each data block on data disks. We extend the data replication protocol between the Primary Node and the Secondary Node. When the data, whose Hash exists in the Hash Repository, is duplication, the block address is transferred instead of the data, and that reduces network bandwidth requirement, saves synchronization time, and improves network efficiency. Keywords: Disaster Recovery, Deduplication, Hash, Duplicate Data.

1 Introduction Today, the ever-growing volume and value of digital information have raised a critical and mounting demand for long-term data protection through large-scale and highperformance backup and archiving systems. The amount of data requiring protection continues to grow at approximately 60% per year[1]. The massive storage requirement for data protection has presented a serious problem for data centers. Typically, data centers perform weekly full backups for weeks to months. Local hardware replication techniques can mask a significant number of failures and increase data availability. For example, RAID can protect against single disk-failure. Furthermore, certain raid levels even survive multiple simultaneous failure[2,3,4]. However, local hardware replication techniques are inadequate for extensive failures or disasters, which may be caused by environmental hazards (power outage, earthquake, and fire), malicious acts or operator errors. To ensure continuous operation even in the presence of such failures, the secondary node (a backup copy of the primary node) is often maintained up-to-date at a remote geographical location and administered separately. When disaster strikes at the primary node, the secondary node takes over transaction processing. The geographic separation of the two copies reduces the likelihood of the backup also being affected by the disaster. Disaster Recovery is such technique. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 68 – 75, 2011. © IFIP International Federation for Information Processing 2011

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Data Disaster Recovery is an important measurement to ensure the integrity and availability of computer systems. With the remote replication technology, an offsite independent backup for the local data is stored via the network[5]. When the local node is damaged, the data can be recovered from a remote system immediately[4,6]. At first, all data blocks on the disk of the local source server (the Primary Node) are duplicated to the remote target server (the Secondary Node) to complete the initial data synchronization. From then on, the data in the Primary Node changed is duplicated to the Secondary Node synchronously or asynchronously via the network[7]. If the data is transferred offsite over a wide area network, the network bandwidth requirement can be enormous[8]. The Primary Node and the Secondary Node are usually deployed separately in two buildings that one is very far apart from the other, or even two cities. There are two ways to transfer the data packet between the nodes, one is then common IP network, the other is the Fibre Channel[9]. Because the private network is expensive, data replication between the Primary Node and the Secondary Node usually uses the common IP network[10]. When the updates are frequent, and the amount of data gets massive, the performance degrades and the backup data maybe lost because of the low network bandwidth and the latency. Some data blocks on the disk are duplication, for example, a file on disk may have multiple copies or different versions, and the great majority is same[11,12]. In the data disaster recovery system, all the data blocks of the file need to be transferred to the Secondary Node when the Primary Node creates a duplicate of the file or updates it. However, the Secondary Node already contains most sections of the data blocks, and the data blocks transferred through the network is duplication of the section of the Secondary Node[13,14,15]. The paper describes the Remote Data Disaster Recovery System using Hash to identify and reduce the amount of data transmitted over the network, and at the same time, the technique assures the reliability and availability of the data, and reduces network bandwidth requirement. The rest of this paper is organized as follows. Section 2 describes the architecture of Disaster Recovery System we used, highlighting the features important for performance. Section 3 discusses implementation of the deduplication method. Section 4 discussed the design and performance evaluation of the solution. Section 5 summarizes and describes future work.

2 Architecture The existing Disaster Recovery System duplicates the data between the Primary Node and the Secondary Node to maintain data consistency of the two nodes through the IP network. An extra storage space is used as a Hash Repository to record the Hash of each data block on the disk. The Hash Repositories of the Primary Node and the Secondary Node are keep consistency, and both of them update synchronously with the data blocks on the disks, as shown in Fig. 1. Generally, when the Primary Node receives the write request, it writes through the local disk immediately and sends the data packets to the Secondary Node. The Secondary node receives the data packets and writes through the disk. After it completes the event, it sends back ACK to the Primary Node, then the event is completed, as shown in Fig. 2[10].

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the Secondary Node File System

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Fig. 2. Write Simultaneously

Typically, the size of the data block is 4KB (4096 Bytes), and the Hash is 16 Bytes (128 bits) that is calculated according to MD5. The Hash Repository storages the Hash of each data block in sequence. Each data block takes 16 Bytes, 16/4096=1/256, so the storage space that Hash Repository takes is 1/256 of the storage space that data blocks take. The architecture of the Hash Repository is shown in Fig. 3. Block0 Block1 Block2 Block3 . . . Blockm . . .

H0 H1 H2 H3 . . . Hm . . .

Fig. 3. Hash Repository

After the Primary Node receives write request to a data block (the Destination Block), it writes the data to the Destination Block, and calculates the Hash of data block to match with the Hash Repository. If they do not match, the Primary Node transfer the data block to the Secondary Node and the Secondary Node writes it to the disk. On the other hand, if they match, it means that the disk of the Primary Node has the same data. This block (the Source Block) is duplication data. It has been delivered to the Secondary Node during the previous initialization or data replication, and it also means that the Secondary Node’s disk already contains data of the source block. In this case, the Primary Node needs to transfer the Source Block Address and the Destination Block Address to the Secondary Node Only. Then, the Secondary Node reads


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the data from the Source Block Address of its local disk and writes it to the Source Block Address. When the transmission succeeds, both the Primary Node and the Secondary Node update their Hash Repositories.

3 Implementation The Primary Node receives a write request to write the data A to the destination block PD_B. Correspondingly, the data A should also be written to the destination block SD_B(PD_B=SD_B) of the Secondary Node, as shown in Fig. 4. The Primary Node does as follows: 1) the Primary Node writes data A to the destination block PD_B; 2) calculates the Hash of the data A; 3) matches the Hash Repository; if it matches with the value in SH_A in the Hash Repository where the Hash of the data block PD_A is placed, it means that the date in PD_A is the same as data A. it is the duplication data. Similarly, the duplication data exists in the Secondary Node also. We suppose that the address is SD_A(SD_A=PD_A). Therefore, when the two nodes synchronize, the data A need not to be transferred. 4) only transfers the source address (PD_A) and the destination address (PD_B) to the Secondary Node; 5) the Secondary Node gets the network package and extracts the addresses from it; 6) reads the data from the source address; 7) writes the data to the destination address; lastly, 8) updates the Hash Repository. the Primary Node Data Disk . . . PD_A SDB Memory Buffer Hash Lib . . . Data A . . PH_A H(PD_A) . . PD_B DDB . . PH_B . . . . . .

the Secondary Node Data Disk . . . Hash Lib SD_A SDB Memory Buffer . . . . . Data A SH_A H(SD_A) . . SD_B DDB . . . SH_B . . . . .

SDB: the Source Data block DDB: the Destination Data block

Fig. 4. Example of Replication

For example: in the existing Disaster Recovery Systems, the file F which size is 8MB (8192KB) is replicated from the A to B. In the 64-bit addressing file system, each data block size is 4KB. The address of each block consists of 8 Bytes (64bit) component, so the file F contains a total of 8192KB/4KB = 2048 data blocks. A total amount of data of synchronization between the Primary Node and the Secondary Node is all the data blocks and their destination addresses, 2048×(4KB+8B) = 8208KB. With the method this paper provides, only the source address, the destination address and the identification information (the size is 1B/block) are transferred because the file F already exists in the Secondary Node. A total amount of data to be

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transferred is 2048×(8B×2+1B)=34KB, so the data to be transferred is 34KB/8208KB≈1/241≈0.415% of the former, and that significantly reduces the network bandwidth requirement for data transmission overhead. When the Primary Node is malfunction, the Secondary Node can start the remote service system to take over the Primary Node service. Before the Primary Node restored, the Secondary Node’s change is written to the disk and the Hash Repository updates at the same time, but this update cannot be synchronized to the Primary Node until it restores. Similarly, When the Secondary Node is malfunction, the Primary Node’s update cannot be synchronized to the Secondary Node. Date resynchronizetion must be performed after the Secondary Node is restored. ID=0

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Fig. 5. Network Package Architecture

Comparing Hash Repositories of the two nodes, we can get the collection of the changed data. The normal node sends these data blocks to the node which used to be malfunction to maintain the consistency between two nodes. During the transfer process, we can use the deduplication technology also. Each data block size is 4KB, and the Hash size is 16 Bytes, so the Hash Repository size is 1/256 of the data disk size. The Address of data block’s Hash in the Hash Repository can be calculated with the following formula: Hash Address=Block Address×16 Similarly, find the Address of Hash in the Hash Repository, the block address can be calculated with the following formula: Block Address= Hash Address/16 When the Primary Node receives write request, it does as follows: A. For the Primary Node 1) Write the data to the disk. 2) For all data block needed to be written, perform the following steps 3) to 5). 3) Calculate the Hash of each data block. 4) Match the Hash in the Hash Repository. •

If it does not match, the Primary Node constructs the network packet, the structure is shown in Fig. 5(a), and transfers it to the Secondary Node. The “ID” in the package is “0” which means the package includes data and the destination address.


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If it matches, the Primary Node calculates the source address with the formula above, constructs the network package, the structure shown as Fig. 5(b), and transfers it to the Secondary Node. The “ID” in the package is “1” which means the package includes the source address and the destination address.

5) Update the Hash Repository. B. For the Secondary Node 1) Receive the network packet from the Primary Node. 2) According to the ID in the network, implement as follows: • •

if ID=0, extract the destination block address and the data block from the network package, and write them to the data disk. if ID=1, extract both the destination block address and the source block address from the network package. Read the source data block from the source block address, and write them to the destination block address.

3) Calculate the Hash of data blocks. 4) Update the Hash of the destination data block in the Hash Repository.

4 Evaluation To evaluate the performance of our solution, we build an experimental system under Linux and compare it with the existing Disaster Recovery System of our lab. To compare fairly, we try our best to create similar experiment environment. In the experiment, there are two machines with the same configurations. We divided the four computers into two groups. One Group is for the existing Disaster Recovery system, the other is for the system with our solution. Each group has two computers. One is the Primary Node and the other is the Secondary Node. The machine’s CPU is Pentium(R) Dual-Core. We deployed 2.0GB RAM in the machine. The disk of the machine is ST3500418AS (500GB) and NIC is Atheros AR8132. The OS we used is Fedora 10 (Linux Kernel 2.6.27). The two nodes are connected with Fast Ethernet. In the experiment, we designed two projects: one is that the transmitted data is divided into blocks which sizes are 4KB, the other is that the transmitted data is divide into blocks which sizes are 2KB. In these experiments, we employ a software packet to simulate to normal operation and record the amount of transmitted data. The results of the experiment are shown in Fig 6. The figure shows that amount of transmitted in the new system is only 22.32% maximum and 12.31% minimum of the former system while divided the date into 4K blocks and it is reach approximately 19% over a period of time (about 20 days). It is only 20.15% maximum and 12.30% minimum of the former system while divided the date into 2K blocks, and it is reach approximately 15% over a period of time. That shows the smaller size of the block can get the higher performance of deduplication. However, there is a question: the smaller size of the block means the greater workload of CPU. It lowered system’s performance.

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'HGXSOLFDWLRQ5DWH

7LPH'D\ .%%ORFNV

.%%ORFNV

Fig. 6. the Rate of Deduplication

5 Conclusion According to the data replication protocol which is extended between the Primary Node and the Secondary Node, When the Primary Node receives a write request to the Destination Block, the Primary Node identifies if it is the duplicate data block according to the Hash. While the data block to be written is the duplication data block, it will not be the data block to be transferred to the Secondary Node but the block addresses which includes the Source Block Address and the Destination Block Address. The Secondary Node reads the data from the Source Block address of it’s local disk and writes to the Destination Block Address. Therefore, when the data is duplication, the Block address is transferred instead of the data block, and that reduces network bandwidth requirement, saves synchronization time, and improves network efficiency. To judge the duplication data makes the CPU’s workload increased and this may make the CPU the bottleneck of the system. Future work includes designing a new method to reduce the CPU’s workload to improve the system’s performance.

References 1. Yang, T., Jiang, H., Feng, D., et al.: DEBAR: A Scalable High-Performance Deduplication Storage System for Backup and Archiving. CSE Technical Reports, 58 (2009) 2. Garcia-Molina, H., Halim, H., King, R.P., Polyzois, C.A.: Management of a remote backup copy for disaster recovery. ACM Transactions on Database Systems 16, 338–368 (1991) 3. Polyzois, C.A., Molina, H.G.: Evaluation of remote backup algorithms for transactionprocessing systems. ACM Transactions on Database Systems (TODS) 19(3), 423–449 (1994) 4. Ellenberg, L.: DRBD 8.0.x and beyond Shared-Disk semantics on a Shared-Nothing Cluster (2007), http://www.drbd.org 5. Ao, L., Shu, J., Li, M.: Data Deduplication Techniques. Journal of Software 21(5), 916– 929 (2010) 6. Reisner, P.: DRBD–Distributed Replicated Block Device (August 2002), http://www.drbd.org


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7. Patterson, R.H., Manley, S., Federwisch, M., et al.: SnapMirror: file-system-based asynchronous mirroring for disaster recovery. USENIX Association (2002) 8. Zhu, B., Li, K., Patterson, H.: Avoiding the disk bottleneck in the Data Domain deduplication file system. In: Proceeding of the 6th USENIX Conference File and Storage Technologies, California, USA, February 2008, pp. 1–14 (2008) 9. Tan, Y.A., Jin, J., Cao, Y.D., et al.: A high-throughput fibre channel data communication service. Institute of Electrical and Electronics Engineers Computer Society, Dalian, China (2005) 10. Reisner, P., Ellenberg, L.: Drbd v8–replicated storage with shared disk semantics (2005), http://www.drbd.org 11. Bobbarjung, D.R., Jagannathan, S., Dubnicki, C.: Improving duplicate elimination in storage systems. ACM Transactions on Storage (TOS) 2, 424–448 (2006) 12. Barreto, J., Ferreira, P.: Efficient locally trackable deduplication in replicated systems. In: Bacon, J.M., Cooper, B.F. (eds.) Middleware 2009. LNCS, vol. 5896, pp. 103–122. Springer, Heidelberg (2009) 13. Aref, W.G., Samet, H.: Hashing by proximity to process duplicates in spatial databases. Presented at Information and Knowledge Management. Gaithersburg, Maryland, United States (1994) 14. Eltabakh, M.Y., Ouzzani, M., Aref, W.G.: Duplicate Elimination in Space-partitioning Tree Indexes. Presented at Scientific and Statistical Database Management (2007) 15. You, L.L., Pollack, K.T., Long, D.D.E.: Deep Store: An Archival Storage System Architecture. In: Proc. Of the 21st Conf. on Data Engineering (ICDE 2005), pp. 804–815. IEEE Computer Society Press, Washington (2005)

A Localization Algorithm for Sparse-Anchored WSN in Agriculture Chunjiang Zhao, Shufeng Wang, Kaiyi Wang, Zhongqiang Liu, Feng Yang, and Xiandi Zhang Beijing Research Center for Information Technology in Agriculture, Beijing, China, 100097 {Zhaocj,Wangsf,Wangky,Liuzq,Yangf,Zhangxd}@nercita.org.cn

Abstract. The location information is very crucial for the sensing data in modern agriculture. However, positioning errors and sparse anchors are two key problems that should first be solved for the localization of the sensor nodes. We proposed a novel algorithm to tackle with these challenges. When the system of adjacent anchor distance equations is ill, a minimized-stress search algorithm (MSS) can decrease positioning error greatly. A collaborative sparse-anchored scheme (CSA) has an excellent positioning effect on low density of anchor, specifically on marginal sensor nodes. Our experimental result verified validity and accuracy of the algorithm. It improved feasibility and cost of WSN positioning technique, significantly. Keywords: WSN, localization, Sparse anchors, Multi-hop cooperation.

1 Introduction Recent advances in micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate in short distances [1]. These sensor nodes with sensing, data processing, and wireless communicating capabilities can be self-organized together in ad-hoc mode and be deployed in pre-determined or random fashion in inaccessible terrains or disaster relief operations. Therefore there are a wide range of applications for wireless sensor networks (WSN): military, infrastructure security, environment and habitat monitoring, industrial sensing, traffic control, etc [2]. Especially, WSN are applied to varied fields in agriculture to improve the agricultural informatization in recent years [3]. In the last decade, WSN have been increasingly applied in modern agriculture [4]. Sensor nodes can be used for monitoring a wide variety of agricultural parameters that include the following phenomena: temperature, humidity, moisture, lightning condition, soil makeup, livestock ID, and so on [5]. However, the sensing data is not meaningful without the company of the sensing location. Naturally, the localization of WSN nodes is very crucial for sensing data usage. Furthermore, accurate location might also be useful for routing and coordination purposes in large scale WSN. The Global Positioning System (GPS) is the most well known location service in use nowadays. The approach taken by GPS, however, is unsuitable for the low-cost, D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 76 – 86, 2011. © IFIP International Federation for Information Processing 2011

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low -power large scale sensor networks nodes in agriculture because of the following reasons: cost, power consumption, inaccessibility, imprecision, size [6]. It is necessary to develop an alternative inexpensive, more applicable localization approach. This paper will present the novel localization algorithm in sparse-anchored WSN. The rest of the paper is organized as follows: The next section gives a brief explanation of theoretical background. Section 3 is our proposed algorithm of localization system. Section 4 describes the resolution of the sparse-anchored problem. Section 5 is experimental results and analysis. Finally, section 6 concludes the paper.

2 Theoretical Background Triangulation, scene analysis, and proximity are the three principal techniques for location sensing [7]. Typically, lateration is the most popular location method that employs triangulation technique. Lateration computes the position of an unknown node by measuring its distance from multiple reference positions. Calculating an object's position in two dimensions requires distance measurements from 3 non-collinear anchors as shown in Figure 1. In 3 dimensions, distance measurements from 4 noncoplanar anchors are required. But, these circles can not intersect at the same point sometime for the error of distance measuring.

Fig. 1. Lateration localization scheme

Fig. 2. Multilateration examples

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The most classic distributed lateration algorithm is AH-Los algorithm proposed by Andreas Savvides, et al. This algorithm defined three operational primitives: atomic

multilateration, collaborative multilateration and iterative multilateration. If an unknown node have three or more neighboring anchors and have measured the distance to neighboring anchors, atomic multilateration can be deployed to determine the position of unknown node. Figure 2(a) illustrates a topology for which atomic multilateration can be applied. The error of estimated position can be expressed as the difference between the measured distance di and the estimated Euclidean distance (Equation 1). The x and y are the estimated coordinates for the unknown node. According to the minimal mean square estimate (MMSE) [8], i.e. Equation 2, the optimal solution of x and y can be obtained. f (x, y) = d − i i

(x − x ) 2 + (y − y ) 2 i i

(1)

n

min(F( x, y)) = min ∑ f i (x, y) 2

(2)

i =1

If a node has three or more neighboring anchors, an over-determined system with a unique solution for the position of unknown node can be yielded. By setting fi(x,y)=0, squaring and rearranging terms, equation 1 became equation 3. x 2 + y 2 − 2x i x − 2y i y = d i2 − ( x i2 + y i2 )

(3)

If unknown node has k neighboring anchors, k equations like equation 3 can be achieved. Then, we can eliminate the x 2 + y 2 terms by subtracting the kth equation from the rest, depicted as equation 4. 2(x k - x i )x + 2(y k - y i )y = d i2 - d 2k − ( x i2 + yi2 ) + ( x 2k + y 2k )

(4)

This system of equations has the form of AX = b and can be solved using the matrix solution for MMSE. Collaborative multilateration can be deployed in the situation where the number of 1-hop neighboring anchors is less than 3, but multi-hop anchors can provide adequate information to locate the position of the unknown node. Figure 2(b) illustrates a basic example. The unknown node 1 has two 1-hop anchors and two 2-hop anchors through the unknown node 2. We can build the system of linear equations like equation 4 and then obtain the solution of equations using MMSE. When an unknown node achieved its position using atomic multilateration or collaborative multilateration, it can inform its neighboring unknown nodes that it has become an anchor. If the informed unknown node satisfies the conditions of atomic multilateration or collaborative multilateration, it can estimate itself position. This process can be iterative until the positions of all the nodes that can have three or more anchors are estimated eventually. This is iterative multilateration principle. In this paper, we proposed novel algorithms to improve position error and position ratio under sparse-anchored condition.


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3 Localization Algorithm AH-Los algorithm eliminates the x 2 + y 2 term in equation 3 by subtracting the kth equation from the rest. Essentially, the solution of system is the intersection of common chord equations between ith circle and kth circle. As depicted in figure 3, equations 5 represent 3 circles with each anchor position as center and the distance from the unknown to the anchor as radius. The first equation subtracted from the second equation gives common chord line L1 and The first equation subtracted from the third equation gives common chord line L2. The intersection of line L1 and the line L2 is the estimated position of the unknown node.

x 2 + y 2 − 2x 1 x − 2y1 y = d12 − ( x12 + y12 ) x 2 + y 2 − 2x 2 x − 2y 2 y = d 22 − (x 22 + y 22 ) x + y − 2x 3 x − 2y 3 y = d − ( x + y ) 2

2

2 3

2 3

2 3

Fig. 3. The intesection of L1 and L2 is the estimated position

Anchor

Real position

Estimated position

Fig. 4. Let different kth as the subtrahend, have different error

(5)

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When this common chord lines is parallel to each other, only small error of estimated anchor position can make a very large error of the intersection position. As demonstrated in figure 4, three anchors upside are close to each other and a anchor downside is far from the other anchors, the solution of equations has higher error. Let different anchor equation as the subtrahend, the solution have different error. The red circle represents the real position of the unknown node. Four red asterisks denote four neighboring anchors of the unknown node. Four blue triangles denote the estimated position with different anchor as the subtrahend. Our algorithm does not eliminate x 2 + y 2 term in equation 3. Setting z = x 2 + y2 , we can get the system of equations as follows:

z − 2x1x − 2y1 y = d12 − ( x 12 + y12 ) z − 2x 2 x − 2y 2 y = d 22 − ( x 22 + y 22 )

(6)

z − 2x i x − 2y i y = d i2 − ( x i2 + y i2 ) The system of equations has the form of AX =b where

⎡1,−2x 1 ,−2y1 ⎤ ⎢ ⎥ 1,−2x 2 ,−2y 2 ⎥ A=⎢ ⎢...... ⎥ ⎢ ⎥ ⎢⎣1,−2x i ,−2y i ⎥⎦

X = [z, x , y]

T

⎡d12 − x 12 − y12 ⎤ ⎢ 2 ⎥ 2 2 ⎢d 2 − x 2 − y 2 ⎥ = and b ⎢ ⎥. ...... ⎢ ⎥ ⎢⎣d i2 − x i2 − y i2 ⎥⎦ −1

The solution can be solved by X = (A A ) A b . At the same time, using T

T

delta = z - x 2 − y 2 as judge condition, we can judge if the system of equations is ill. When the condition number of the equations is very large, the delta is also very large. This means the position error is too large to locate the node. We proposed the minimized-stress search localization algorithm (MSS) to tackle with this situation. Before presenting the algorithm, we first make some definitions. Definition 1. The distance between the current position of the unknown (x,y) and the ith neighboring anchor (xi,yi) is dicur and the measured distance to ith neighboring → anchor is di .The stress from ith anchor is F i where →

Fi = ((1 − d i / d icur ) * ( x i − x ), (1 − d i / d icur ) * ( y i − y ))

As dicur,> di , the direction of vice versa.

→

Fi

(7)

is pointed to the ith anchor from current position,


Definition 2. The resultant stress of the unknown,

nent stress

→

Fi

→

F

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, is composed of every compo-

. →

F

=

→

F

1

→

+

F

2

+

+

...

→

F

i

(8)

The process of the MSS algorithm is described as follows: Step 1: when the system of equations 6 is ill, we first select two anchors which the distance between them is the farthest and then compute the intersection of two anchor circles which radius is the measured distance from anchor to unknown node. Step 2: We select each of intersection as search original position and compute the → each component stress F i and then composed the resultant stress F→ by equation 7 and equation 8, respectively. → Step 3: Pulled by the resultant stress F , each estimated position move to new position (xn,yn). Next, we judge if the new position have less distance error than the old position As shown by equation 9,

→ → F x , F y ,Lstep

denotes x axis component , y axis

component and the steplength for moving, respectively. →

x n = x 0 + L step * F x →

y n = y 0 + L step * F y

(9)

k

Xigma = ∑ (d icur − d i ) 2 i =1

Step 4: If Xigma now < Xigma old , x 0 ← x n , y 0 ← y n , Otherwise steplength ← steplength / 2 and repeat step 2 for several times. Step 5: when estimated position cannot move on, we turn F→ 90 degrees clockwise or 90 degrees counter clockwise to test a new marching direction. If we find a new direction, step 2 is repeated again. Otherwise the process is terminated. So we estimated two possible final positions. Step 6: We compare the two final distance residuals and then select the final position with the smallest residual as the position of the unknown node.

Fig. 5. The example of MSS algorithm process

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The MSS algorithm have overcome the defect of higher position error when the equations is ill-conditioned and conquered the drawback of one starting point that is ease to get in the local minimum. A sample process of MSS algorithm is demonstrated in the figure 5.

4 Sparse-Anchored Localization Algorithm After iterative multilateration localization is repeated, The position of the unknown nodes that only have two anchors or one anchor eventually can not be determined We proposed to utilize the collaboration of its one or two anchors to locate the position of the unknown. Under the condition of only two adjacent anchors, the unknown can estimate itself position U or U’, as shown in figure 6.

Rmax Rmax

A3

A4 Uÿ

A2

A1

U

Unknown node

Anchor

Fig. 6. Localization algorithm with only two 2-hop anchors

The unknown sent the two possible positions, U and U’ to its 1-hop anchors, i.e. A1 and A2, and then the one-hop anchors pass the possible positions to the 2-hop anchors, i.e. A3.and A4. The two-hop anchors will judge if U or U’ is in its maximum sensing range, Rmax. Once either of the two possible positions belongs to the maximum sensing range of A3 or A4 by computing distance, the unknown node is informed that this position is excluded from the estimated position because A3 or A4 have not been its 1-hop anchor. When each of U and U’ can not be excluded, their midpoint is taken as the estimated position. This method can be deployed in the larger scale, such as 3-hop scale or multi-hop scale. Another case is where the unknown node only has a 1-hop anchor. The previous algorithm will be helpless. We will resort to another method to locate the position approximately.


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I4 I3

Rmax

I5 I2 d

Unknown node

I1

I6

Anchor

Intersection point

Estimated position

Fig. 7. The Localization algorithm with only one 1-hop anchor

As shown in figure 7, the unknown node has one 1-hop anchor and three 2-hop anchors. The circle with the distance d as radius has six intersection points with the maximum sensing range of three 2-hop anchors, i.e. I1, I2, I3, I4, I5 and I6,. We can compute the distances from the intersection points to three 2-hop anchors, respectively. Once the distance for the intersection point is less than Rmax (the largest sensing range), the intersection point is excluded. Finally, the intersection points, I1 and I6, are left. So the estimated position is on the pink arc from I1 to I6. We can take the midpoint of the arc or the midpoint of the line from I1 to I6 as the estimated position. As this scheme has a larger error, the estimated position should not be taken as anchor in the iterative process.

5 Experimental Results To verify our proposed localization algorithm, we randomly generate a scenario with 200 nodes within a square field (100x100) in Matlab. These nodes are deployed randomly in the field and can measure the distances to the adjacent nodes in the sensing range R by RSSI or other ranged methods. The anchor ratio to all nodes is Aratio. To simulate real ranged error, the true distances (d) are blurred with Gaussian noise, er. So the measured distance have the distribution, d*(1+N(0, er)). When the transmission range of the nodes(R), the range error(er) and anchor ratio (Aratio) is set to 15, 5% and 10%, respectively, the topology is shown in figure 8. The blue triangles represent the anchors, the red circles represent the unknown nodes, and the azury lines represent the wireless connections between the nodes. Figure 9 shows the positioning result of our MSS and CSA algorithms. The starting point of the blue arrows represents the estimated position and the end point of the blue arrows represents the real position. The longer the blue arrow is, the larger the positioning error is.

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When there are approximate 9 connectivity degree and 10 percent anchor ratio, AH-Los algorithm can achieve 90 percent position ratio and 6-7% position error (about 20 cm) [9,10]. But under sparse-anchored conditions, there are higher position error and lower position ratio.

Fig. 8. The topology with R=15, er=5% and Aratio=10%

Fig. 9. Final position estimation result

Under the same situation, our algorithms have a higher positioning ratio of 100% and lower average positioning error of 2.45%. Anchor density has a significant effect on the positioning ratio and error. Contrast to the AH-Los algorithm, the positioning ratio was shown with various anchor density


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in figure 10. As shown, when the percentage of anchors is low, our MS-CSAL algorithm substantially increased the positioning ratio. These algorithms can not only effectively decrease the number of anchors to lower the cost of WSN, but also improve the localizing of the unknown node on the edge of the networks. AH-Los algorithm

our SM-CSAL algorithm

100 90 o i t a R g n i n o i t i s o P

80 70 60 50 40 30 20 10 0

5

15

25

40

50

60

70

80

90

100

Anchor Percentage

Fig. 10. Effect contrast between AH-Los and MSS-CSPL

6 Conclusion We have proposed a novel MSS-CSA algorithm for WSN positioning in agriculture. The minimized-stress algorithm improved the positioning precision greatly when the system of multi-anchors positioning equations is ill. The collaborative sparse-anchored localization algorithm has solved the positioning problem of anchor deficiency, special for the unknown node on the edge of WSN in agricultural positioning. Our simulation experiments have verified the effect of the algorithm in terms of positioning ratio and positioning errors. Our future work will be concentrated on the Zigbee-based implementation and analysis of error propagation in agricultural positioning.

Acknowledgments This work is supported by National 11th Five-year Plan for Science & Technology of China under Grant no. 2009BADB6B02.

References 1. Hautefeuille, M., O’Mahony, C., O’Flynn, B., Khalfi, K., Peters, F.: A MEMS-based wireless multisensor module for environmental monitoring. Microelectronics Reliability 48(6), 906–910 (2008) 2. Mariño, P., Fontán, F.P., Domínguez, M.A., Otero., S.: Viticulture zoning by an experimental WSN. International Journal of Information Technology and Web Engineering 4(1), 14–30 (2009)

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3. O’Shaughnessy, S.A., Evett, S.R.: Developing wireless sensor networks for monitoring crop canopy temperature using a moving sprinkler system as a platform. Applied Engineering in Agriculture 26(2), 331–341 (2010) 4. Matese, A., Di Gennaro, S.F., Zaldei, A., Genesio, L., Vaccari, F.P.: A wireless sensor network for precision viticulture: The NAV system. Computers and Electronics in Agriculture 69(1), 51–58 (2009) 5. Siuli Roy, A.D., Bandyopadhyay, S.: Agro-sense: precision agriculture using sensor-based wireless mesh networks. In: Proceedings of the First ITU-T Kaleidoscope Academic Conference. Innovations in NGN. Future Network and Services, pp. 383–387 (2008) 6. Heraud, J.A., Lange, A.F.: Agricultural Automatic Vehicle Guidance from Horses to GPS: How We Got Here, and Where We are Going. ASABE Distinguished Lecture Series, pp. 1–67 (2009) 7. Yuan, L., Choi, L., Chin, F.: Construction of local anchor map for indoor position measurement system Zhou. IEEE Transactions on Instrumentation and Measurement 59(7), 1986–1988 (2010) 8. Savvides, A., Han, C.-C., Srivastava, M.B.: Dynamic fine-grained localization in ad-hoc networks of sensors. In: Proc. of the 7th Annual Int’l Conf. on Mobile Computing and Networking, pp. 166–179. ACM Press, Rome (2001) 9. Greene, W.: Econometric Analysis, 3rd edn. Prentice-Hall, Englewood Cliffs (1997) 10. Wang, F.-B., Shi, L., Ren, F.-Y.: Self-localization systems and algorithms for wireless sensor networks. Ruan Jian Xue Bao/Journal of Software 16(5), 857–868 (2005) 11. Savvides, A., Park, H., Srivastava, M.B.: The bits and flops of the n-hop multilateration primitive for node localization problems. In: Proceedings of the ACM International Workshop on Wireless Sensor Networks and Applications, pp. 112–121 (2002)

A New Method of Transductive SVM-Based Network Intrusion Detection Manfu Yan1 and Zhifang Liu2 1

Department of Mathematics, Tangshan Teacher’s College, Tangshan Hebei, China [email protected] 2 Network Technology Center, Tangshan Teacher’s College, Tangshan Hebei, China [email protected]

Abstract. Based on the existing Transductive SVM and via introducing smooth function P ( Δ, λ ) to construct smooth cored unconstrained optimization problem, this article will build the optimization model accessible to degenerate solutions to generate an improved transductive SVM, introduce simulated annealing to degenerate the optimization problem, and apply such a Support Vector Classifier to generate a new method of network intrusion detection. Keywords: optimization; unconstrained problem; transductive SVM; network intrusion detection; simulated annealing.

1 Introduction Network intrusion detection is a safe mechanism with dynamic monitoring, prevention or resistance against network intrusion [1]. The network-intrusion detection system can be used to discover and identify the behavior and attempt of intrusion in the system via monitoring and analyzing the network flow and system audit records to give out an alarm of intrusion in order to facilitate the administer to take effective measures to mend the loopholes of the system and fill up the system [2]. Network intrusion detection is used to separate user behavior’s normal data of from its abnormal data, which essentially can be regarded as the classification. The data to describe the behaviors of users is of multi-index as usual. Therefore, it can be expressed with an n-dimensional vector. In this way, the network-intrusion detection problem can be summarized as data group for normal or abnormal behavior of users, i.e. two kinds of classification problems of n-dimensional vector, which can help create the detection methods and system via the support vector classifier. But studies and practices indicate that as to the network-intrusion detection problem, the methods and system built by the use of ordinary SVM (e.g. C-SVM) are not desirable in the precision of detection. Thus, we try to apply the transductive support vector classifier to create the detection methods and introduce the simulated annealing method to degenerate the optimized model.

2 The Improvement of Transductive Support Vector Machine Generally, for Support Vector Machine, it is set up by given trainingset T = {( x1 , y1 ) ,L , ( xl , yl )} D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 87 – 95, 2011. © IFIP International Federation for Information Processing 2011

(1)

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Here, xi ∈ X = R n , yi ∈ Y = {1, −1} , i = 1,2,L , l . We normally call them Inductive Support Vector Machine [3]. Vapnik has discussed a kind of sorting algorithm for Transductive Support Vector Machine, it is different from Inductive Support Vector Machine. It provides a mutual independent set, which follows joint distribution, besides the given trainingset T.

，x } ,

S = { x1* , …

* m

(2)

Here x1* ∈ X = R n . For TSVM, we want to find a optimized function f ( x, w0 ) from a particular function set F = { f ( x, w )} , so that the risk R ( w) =

(

1 m ∑ L yi* , f ( xi* , w) m i =1

)

(3)

is minimized. Here w is a general parameter of the function, L ( y, f ( x, w) ) represents the loss due to estimation of y by f ( x, w) , that is to say, here we interested in the function value of f ( x, w0 ) on given fixed points xi* , not all the function values within the field of definition. 2.1 Unconstraint Problem Initially, the optimization of TSVM is [4]

min

w∈H ,b∈R , y*∈R ,ξ ,ξ *

l m 1 2 w + C ∑ ξi + C * ∑ ξ *j 2 i =1 j =1

(4)

s.t. yi ( ( w ⋅ xi ) + b ) ≥ 1 − ξi , i = 1, ⋅⋅⋅, l , y*j

(5)

( ( w ⋅ x ) + b ) ≥ 1 − ξ , j = 1, ⋅⋅⋅, m, * j

* j

(6)

ξi ≥ 0, i = 1, ⋅⋅⋅, l ,

(7)

ξ *j ≥ 0, j = 1, ⋅⋅⋅, m,

(8)

—(8) in this section, it is reduced to un-

We would like to simplify initial problem (4) constraint problem.

—(8), it must satisfy

Theorem. Consider the solution for (4) y

* j

(( w ⋅ x ) + b) ≥ 0 * j

(9)

for all x*j * * * Prove: Suppose the solution for the problem is ( w , b , ξ , ξ , y 1 , ⋅ ⋅ ⋅, y m )

( w ⋅ x ) + b ≥ 0 , then y * j

* j

= 1 Since when y = 1 , y * j

* j

(

minimized objective function, it must satisfy ξ = 0 or * j

)

，if for some x

( w ⋅ x ) + b ≥ 0, ξ ≥ 1 − y * j

0 ≤ ξ = 1− y * j

* j

* j

* j

(

(w⋅ x ) + b * j

(( w ⋅ x ) + b ) < 1 ． * j

* j

,

) , as

A New Method of Transductive SVM-Based Network Intrusion Detection

89

* When y j = −1 , y*j ( ( w ⋅ x*j ) + b ) ≤ 0, ξ *j ≥ 1, which is large than then objective function

value when y*j = 1 .

* Similarly, for some x*j , ( w ⋅ x*j ) + b ≤ 0 , then y j = −1

(

)

Namely, for all xj , y j ( w ⋅ x j ) + b ≥ 0, Based upon the theorem above, we can change the constraint (6) and (8) of problem (4)—(8) into *

*

*

(

)

ξ ∗j = 1 − (w ⋅ x ∗j ) + b + ,

j = 1,2, L, m

(10)

However, for variable ξi , i = 1,L , l , we got ξ j = (1 − y i ((w ⋅ xi ) + b ))+ ,

i = 1,2, L , l

(11)

Here, function (⋅ ) is single variable function, +

⎧Δ, Δ ≥ 0; (Δ ) + = ⎨ ⎩ 0, Δ < 0

Base on this, we could convert problem (4) min w,b

(12)

—(8) into unconstraint optimization.

l m 1 2 w + C ∑ (1 − yi (( w ⋅ xi ) + b)) + + C * ∑ (1 − ( w ⋅ x*j ) + b ) +⋅ 2 i =1 j =1

(13)

2.2 Smooth Unconstraint Problem Since unconstraint problem(13) is not smooth it is not able to be solved by regular optimization method. As a result, we think about modifying the second and third part of objective function for problem (13), so that it becomes smooth, in order to construct a smooth unconstraint problem that is similar to unsmooth and unconstraint problem (13). Because of that, we introduce an approximate function for unsmooth function (Δ) + P ( Δ, λ ) = Δ +

1

λ

ln(1 + e −λΔ ),

(14)

Here, parameter λ >0, obviously the function above is smooth; we could prove it as well. When λ → ∞ , function P (Δ, λ ) converges at (Δ) + such that the second part of unconstraint optimization (13) is transformed into l

C ∑ P (1 − yi (( w ⋅ xi ) + b), λ ). i =1

(15)

and the third part is transformed to m

C * ∑ P(1 − ( w ⋅ x*j ) + b , λ ) j =1

(16)

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The unsmooth term Δ′ is still inside (16), so we decide to use following function to approximate Δ′ smoothly. P′( Δ′, μ ) = Δ′ +

1

μ

ln(1 + e− 2 μΔ′ )

(17)

We could deduce some the following theorem by making use of the properties of P (Δ, λ ) : Now, unconstraint problem (13) approximates optimization problem min w,b

l m 1 2 w + C∑ P(1− yi ((w⋅ xi ) + b), λ) + C* ∑P(1− P′((w⋅ x*j ) + b, μ), λ). 2 i =1 j =1

(18)

When λ , μ is large enough, the solution of smooth unconstraint problem (18) will most approximate unsmooth unconstraint problem (13). 2.3 Smooth Unconstraint Problem with Kernel If we take account of linear partition of input space, we could introduce a mapping from input space X to Hilbert space H Φ:

X →H

(19)

x → X = Φ ( x)

and kernel function

K ( x, x′) = (Φ ( x) ⋅ Φ ( x′)),

(20)

Apply l1 module w 1 on objective function of problem (4)---(8), we got optimization problem l

m

i =1

j =1

min w + C ∑ ξ + C * ∑ ξ * i j 1

(21)

s.t. yi (( w ⋅ xi ) + b) ≥ 1 − ξ i , i = 1,L , l ,

(22)

y *j (( w ⋅ x*j ) + b) ≥ 1 − ξ *j , j = 1,L, m,

(23)

ξi ≥ 0, ξ *j ≥ 0, i = 1,L, l , j = 1,L, m.

(24)

w,b ,ξ ,ξ *

—

We know that if β , β * is the solution of dual problem for problem(4) (8), then the solution of initial problem(21) (24) to W could approximately represented as

—

l

m

l

m

i=1

j =1

i =1

j =1

W = ∑ yi βi Xi + ∑ y*j β *j X *j = ∑(αi −αi )Φ(xi ) + ∑(α*j −α*j )Φ(x*j ).

(25)

—(24) to following problem, by making use of above

We could alter problem (21) expression


l

min

α ,α ,α * ,α * ,b ,ξ ,ξ * ,

∑ (α i =1

i

m

l

m

j =1

i =1

j =1

+ α i ) + ∑ (α *j + α *j ) + C ∑ ξi + C * ∑ ξ *j

l

m

k =1

k =1

yi (∑ (α k − α k ) K ( xk , xi ) + ∑ (α k* − α k* ) K ( xk* , xi ) + b) ≥ 1 − ξi , i = 1,L , l ,

l

m

k =1

k =1

91

(26) (27)

y*j (∑ (α k − α k ) K ( xk , x*j ) + ∑ (α k* − α k* ) K ( xk* , x*j ) + b) ≥ 1 − ξ *j , i = 1,L , l ,

(28)

ξ i ≥ 0, i = 1,L , l ,

(29)

ξ *j ≥ 0, j = 1,L , m,

(30)

l

m

i =1

j =1

Here we use ∑ (α i + α i ) + ∑ (α *j + α *j ) to replace w 1 . The method is similar to that of previous section we could transform problem (26) (30) into smooth unconstraint optimization problem by introducing smooth function P(Δ, λ ) and P ′(Δ ′, μ ) .

—

l

min

α ,α ,α * ,α * ,b ,ξ ,ξ * ,

∑ (α i =1

m

i

+ α i ) + ∑ (α *j + α *j ) j =1

l

l

m

i =1

k =1

k =1

+C ∑ P (1 − yi (∑ (α k − α k ) K ( xk , xi ) + ∑ (α k* − α k* ) K ( xk* , xi ) + b ), λ ) m

l

m

j =1

k =1

k =1

(31)

+C * ∑ P(1 − P′(∑ (α k − α k ) K ( xk , xi ) + ∑ (α k* − α k* ) K ( xk* , x*j ) + b), μ ), λ )

— ，

When λ , μ is large enough, above problem is similar to problem(26) (30) consequently, we could create decision function after we got the optimum solution *

*

*

( α , α , α , α , b , ξ , ξ , )of this problem. l

m

k =1

k =1

f ( x ) = sgn(∑ α k − α k ) K ( xk , x) + ∑ (α k* − α k* ) K ( xk* , x) + b),

(32)

Additionally, using this decision function to decide the category of the points in test set S.

：Improvement of TSVM

2.4 Conclusion

Assume known trainingset T = {( x1 , y1 ) ,L, ( x1 , y1 )} , here xi ∈ X = R n , yi = {−1,1} , i = 1,L, l ; known test set S = { x1* ,L , xm* } , here xi* ∈ X = R n ; b) Choose suitable parameter C and C * , choose suitable kernel function K ( x, x′ ) ; construct and find the unconstraint problem (31), thus got optimum solua)

tion (α, α , α* , α * , b) ; c) Create decision function:

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m

k =1

k =1

f ( x) = sgn(∑ αk − αk ) K ( xk , x) + ∑ (αk* − αk* ) K ( xk* , x) + b)

Thereby, for any test point belongs to S, the decision function will provide the category for it.

3 New Method on Network Intrusion Detection As the objective function of problem 31 has a continuous gradient and hesse matrix, as well as unconstrained, it can be solved by basic algorithm to unconstrained problem. However its objective function is not convex function thus a number of local optimal solutions may exist, and through the general unconstrained algorithm may not acquire global optimal solution. In the following global optimal algorithm – simulated annealing algorithm will be introduced to solve problem (31), and the model will be introduced to network intrusion detection. 3.1 Simulated Annealing Algorithm First, we will introduce simulated annealing algorithm. Simulated annealing algorithm is a kind of random search method known as Monte Carlo method, which allows the objective function to have random changes in the increasing direction. Therefore, simulated annealing algorithm can jump out of local minimum point. This algorithm was proposed by Metropolis as early as 1953, originated from simulation to solid annealing process. The annealing process starts at a certain high enough temperature, and almost every random motion are acceptable under this temperature. Then the temperature decreases slowly according to some cooling rule and tends to zero. Enough time is needed for the system to reach a stable state at each temperature point, and finally places in a state with lowest energy, to obtain a relative global optimal solution to the optimization problem. In which, one solution xk to the optimization problem and its target value f(xk) correspond to a solid microstate k and its energy Ek respectively. The temperature T in the annealing process is a control parameter decreasing by the algorithm process. The algorithm adopts Metropolis acceptance criteria. In each step of the algorithm, a new candidate solution generates randomly. If the new solution decreases the objective function, it is acceptable; otherwise whether to accept it will be decided in form of exponential probability. Probability P to accept the new solution is: ⎧ exp(− Δf / T ) p=⎨ ⎩1

− Δf > 0, − Δf ≤ 0.

(33)

In which, Δf is the variation of objective function caused by random disturbance and T represents temperature. From formula (33) we can see that for a given Δf, when T is relatively high, acceptance probability to the new solution which increases the function is larger than the probability when T is relatively low. Thus the entire algorithm keeps the iterative process of “generate new solution – judge – accept or discard” till find the optimal solution finally. The specific algorithm is as follows: Algorithm A. Simulated annealing algorithm a)

Suppose k=0, T= T0, in which T0 is the initial temperature. Parameter L and initial value x0 are given;


b) c) d)

e) f)

93

Generate a new candidate xk+1 by random disturbance of xk; Calculate Δf = f(xk+1) - f(xk); If Δf≤0, accept the new solution xk+1=xk. If the stop criteria is satisfied, the algorithm stops and x= xk+1; otherwise give a random value λ in the range of 0 to 1 obeying uniform distribution. If exp(-Δf/T)>λ, accept the new solution xk+1=xk; Suppose k=k+1, if k≤L, jump to step b); Decrease T0 according to temperature cooling rule. Suppose x0=xk and k=0, and jump to step b).

In the above algorithm, parameters we need to select include the initial temperature value T0. Simulated annealing algorithm requires a large enough T0 to ensure jumping out of the local optimal solutions, that is to ensure exp(-Δf/T0)≈1. Selection of a too large T0 will cause a too long algorithm period, while a too small T0 will cause the algorithm traps in the local optimal solution too early. The other parameters need to be selected are iteration times L under each temperature, initial solution x0. Besides the decreasing rule of temperature T also needs to be known, generally taken as Tk+1=βTk,0 O*, (3) P(x') > P*, add x' to the solution set Q and delete those solutions being dominated by x' from Q, and update E*, O*, or P*. If E(x') > E*, update the tabu list T and let x = x', go Step 2.

A Tabu Search Approach to Fuzzy Optimization of Camellia Oleifera Fertilization

129

Step 3. If no such a solution satisfying E(x') > E* and all the neighborhood solutions are tabu, or some of the termination conditions are reached, the algorithm stops and returns Q. Step 4. Otherwise, select an x' in N(x)\T with the best expected value in E(x'), update the tabu list T and let x = x', go Step 2. In the above algorithmic framework, the neighborhood of a fertilizer solution [x1, x2, x3] is defined as the set of six solutions including [x1±0.1, x2, x3], [x1, x2±0.1, x3] and [x1, x2, x3±0.1], where 0.1 is a preset increment value (for other yield response models, the increment value can be adjusted according to the units of measurement used and the fertilizer application quantities estimated). We run the tabu search algorithm for solving the Camellia oleifera yield response model. The credibility level λ was set to 0.25; the algorithm stopped after 528 iterations, and the result non-dominated solution set contained 6 solutions as shown in Table 2. As we can see, solution #5 and #6 reached the maximum expected yield value 1162.5, in which #6 reached the maximum optimistic value 1295, and #1 reached the maximum pessimistic value 1036. Table 2. Non-dominated solution set for the Camellia oleifera yield response model

#1 #2 #3 #4 #5 #6

N 6.6 6.8 7.1 7.3 8.4 8.4

P 11.7 11.8 12.1 12.3 13.4 13.6

K 25.0 25.0 25.0 25.0 25.0 25.0

Y (931, 1140, 1365) (929, 1141, 1369) (924, 1143, 1380) (921, 1145, 1386) (905, 1158, 1429) (902, 1158, 1432)

E(Y) 1144 1145 1147.5 1149.25 1162.5 1162.5

O(Y) 1252 1255 1262 1266 1294 1295

P(Y) 1036 1035 1034 1033 1032 1030

5 Conclusion The paper establishes a fuzzy mathematical model between Camellia oleifera yield and fertilization application rates, in which variation coefficients of N, P, K are described with fuzzy numbers. In particular, we present a tabu search algorithm for finding the non-dominated fertilization solution set on three fuzzy measures including expected value, optimistic value and pessimistic value of the Camellia oleifera yield. Our approach is more realistic and practical by taking vague and imprecise data into consideration, and supports more comprehensive decision-making by generating a set of high-quality alternatives. The fuzzy yield response model can be applied to a wide variety of crops more reasonably and effectively, and the algorithmic framework can be applied/extended for solving the quadratic and other kinds of models. Moreover, more fuzzy ranking criteria can be included in order to providing more comprehensive and complicated decision support. Our ongoing work also includes developing an integrated software tool to support fuzzy data analysis, regression modeling, problem solving, and visualized fertilizer decision-making.

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Acknowledgments. The work was supported by the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (PHR200907136).

References 1. Cerrato, M.E., Blackmer, A.M.: Comparison of models for describing corn yield response to nitrogen fertilizer. Agronomy J. 82, 138–143 (1990) 2. Zadeh, L.A.: Fuzzy sets. Information & Control 8, 338–353 (1965) 3. Kandala, V.M., Prajneshu: Fuzzy regression methodology for crop yield forecasting using remotely sensed data. J. Indian Soc. Remote Sensing 30, 191–195 (2002) 4. Yu, N., Zhang, Y.L., Zou, H.T., Huang, Y., Zhang, Y.L., Dang, X.L., Yang, D.: Fuzzy evaluation of different irrigation and fertilization on growth of greenhouse tomato. In: 2nd Int’l Conf. Fuzzy Information and Engineering, Guanzhou, China, pp. 980–987 (2007) 5. Li, M., Fang, D., Zhang, J., Zhang, Q.: A new approach for fuzzy fertilization forecast based on support vector learning mechanism. In: 4th Int’l Conf. Fuzzy Systems and Knowledge Discovery, Haikou, China, vol. 2, pp. 321–325 (2007) 6. Palaniswami, C., Dhanapal, R., Upadhyay, A.K., Manojkumar, C., Samsudeen, K.: A fuzzy neural network for coconut yield prediction. J. Plant. Crop. 36, 24–29 (2008) 7. Dubois, D., Prade, H.: Operations on fuzzy numbers. Int. J. Sys. Sci. 9, 613–626 (1978) 8. Kaufmann, A., Gupta, M.M.: Introduction to Fuzzy Arithmetic Theory and Applications. Reinhold, Van Nostrand (1991) 9. Liu, B.: Uncertainty Theory, 2nd edn. Springer, Berlin (2007) 10. Lee, H., Tanaka, H.: Fuzzy approximations with non-symmetric fuzzy parameters in fuzzy regression analysis. J. Oper. Res. Soc. Japan. 42, 98–112 (1999) 11. Kwang, H.L., Lee, J.H.: A method for ranking fuzzy numbers and its application to decision-making. IEEE Transactions on Fuzzy Systems 9, 677–685 (1999) 12. Glover, F.: Tabu search, part I. ORSA J. Comput. 1, 190–206 (1989) 13. Glover, F.: Tabu search, part II. ORSA J. Comput. 2, 4–32 (1990) 14. Cvijovic, D., Klinowski, J.: Taboo search: an approach to the multiple minima problem. Science 267, 664–666 (1995) 15. Hansen, M.: Tabu search for multiobjective optimization: MOTS. In: 13th Int’l Conf. Multiple Criteria Decision Making, Cape Town, South Africa (1997) 16. Zheng, Y.J.: Extended tabu search on fuzzy traveling salesman problem in multi-criteria analysis. In: Chen, B. (ed.) AAIM 2010. LNCS, vol. 6124, pp. 314–324. Springer, Heidelberg (2010)

AgOnt: Ontology for Agriculture Internet of Things Siquan Hu1, Haiou Wang1, Chundong She2, and Junfeng Wang3 1

School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China 2 University of Electronic Science and Technology of China 3 College of Computer Science, Sichuan University [email protected]

Abstract. Recent advances in networking and sensor technologies allow various physical world objects connected to form the Internet of Things (IOT). As more sensor networks are being deployed in agriculture today, there is a vision of integrating different agriculture IT system into the agriculture IOT. The key challenge of such integration is how to deal with semantic heterogeneity of multiple information resources. The paper proposes an ontology-based approach to describe and extract the semantics of agriculture objects and provides a mechanism for sharing and reusing agriculture knowledge to solve the semantic interoperation problem. AgOnt, ontology for the agriculture IOT, is built from agriculture terminologies and the lifecycles including seeds, grains, transportation, storage and consumption. According to this unified meta-model, heterogeneous agriculture data sources can be integrated and accessed seamlessly. Keywords: Agriculture Internet of Things, Ontology, Semantics.

1 Introduction Recent advances in networking, sensor and RFID technologies allow connecting various physical world objects to the IT infrastructure, which could, ultimately, enable realization of the Internet of Things (IOT) and the Ubiquitous Computing visions [1] [2] [3]. IOT has great potential in Agriculture. Consider the future vision of the food lifecycles are well recorded from seeds, cultivation, products, transportation, food processing, sales in supermarket, it is exciting to have public confidence on food security and tremendous additional value to the agriculture and food suppliers. However, in the agriculture internet of things, the data are sourced from different organizations and information technology facilities; it is an enormous challenge to integrate them into a workable system so that the midstream firms and the end consumers can query the history of the agricultural products unhindered by the bounds of the previous venders. One of the most important requirements of such integration is that the data semantics are consistence among the different phase of the products. To achieve such target, a unified ontology of agriculture products should be utilized by all the information systems of the different phases. Ontology is a new D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 131–137, 2011. © IFIP International Federation for Information Processing 2011

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concept that is emerging from the various Semantic Web initiatives, which roughly speaking can be defined as a semantic system that contains terms, the definitions of those terms, and the specification of relationships among those terms. In this paper, we proposed AgOnt - agriculture ontology for the purpose of agriculture internet of things. To keep the ontology light-weighted, we ignore the complexity of the specific agriculture activities or food processing; only the environments of the agriculture products are paid close attention to so that all the history can be queried by the follow-ups users. The remainder of the paper is organized as follows: section 2 presents the design of proposed AgOnt ontology. In section 3 the query of AgOnt based knowledge base is discussed. Section 4 highlights existing related work briefly. Section 5 concludes the current work and discusses possible future avenues for this research.

2 AgOnt Ontology Ontology gives formal description on the hierarchical categories for real world knowledge [4], [5], [6]. Our approach uses ontology to capture the semantics of agriculture grains and their cultivation, logistics, storage history in the application of agriculture IOT. The purpose of building this ontology is to provide a mechanism for semantic interoperation between different systems (clouds) in the global agriculture clouds computing system. The semantic integration based on the ontology provides a solid basis for the integration of heterogeneous agriculture information system to form a huge information platform to record the grain lifecycles from the seeds, plant cultivation to food consumption. AgOnt is based on the IEEE Suggested Upper Merged Ontology (SUMO) [7], which is the largest formal public ontology in existence today and used widely for research and applications in search, linguistics and reasoning. To capture the semantics of the grain or food lifecycle terminologies, we not only are able to define the environment of a product, but also describe the recall relationship between the succeeding forms such as the seeds and the plant, the wheat and the ponder, etc. 2.1 Structure of the Ontology We create AgOnt ontology on the basis of two kinds of relationships in the lifecycle of agriculture products. One is the relationship of a product and its properties such as location, timestamp, environments parameters, processing status, etc. The other is the relationship between a product and its source products such as a plant and its seedlings where the plant grows from. Currently, we have defined 3 relationships between entities in our ontology generation: Is-a, Has-property, and Source-from: z z z

Is-a: entity A is an instance of entity B. Has-property: entity A has a property B. Source-from: entity A sources from entity B.

To describe and maintain the knowledge cleanly, we identify 5 main primitive domain classes as the top level ontology of the AgOnt as in Fig. 1.

AgOnt: Ontology for Agriculture Internet of Things z

z z z z

133

Product class is used to describe the instance of agriculture products such as seeds, wheat, ponder, etc. It is the core of the whole ontology, which is a reflection of the view of thing-focused in the internet of things. Phase is a simple class to capture the conception of position in the lifecycle of the agriculture product. Phase is a property of any product. Time class is a supplement for phase to describe the concrete timestamp of the product activity. It is a property of any product. Location class is to capture where the product is and what organization is responsible for the maintenance of the agriculture phase Condition class captures the environment parameters of the product.

“Thing” is a dummy root for all classes in the ontology hierarchy. To illustrate the relationship “source-from”, Fig. 1 gives an example between “Seed” and “Seedling” to show the grow-up or evolution of agriculture product. It is obvious that “Seed” and “Seedling” are the second level ontologies.

Fig. 1. The Top Level ontology of AgOnt

The Product class hierarchy is showed in Fig. 2. All agriculture products are dived into 5 subclasses, Seed, Seedling, Plant, Crop and Processed food, with each sources from previous. Crop capture the concept of the product cropped directly from the field. Different from it, Processed food describe the product produced by food processing factories. The Phase class describe the abstract activity of the product ignore the specific characteristics. Currently we have 6 kinds of phases showed in Fig.3. The Condition class captures all the sensor output to log the environment data of the product. Depending on the phase, the conditions of a product may have different properties. These property data are captured by smart sensors and can be propagated into the IOT. Fig. 4 shows a snippet of the conditions.

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Fig. 2. The extended Product ontology

Fig. 3. The extended Phase ontology

Fig. 4. The extended Condition ontology

3 Querying on Agriculture Knowledge Base Based on AgOnt Ontology The AgOnt ontology provides a logical base where the semantics of the history of an agriculture product. Different agriculture information systems can integrate into a self-consistent knowledge base via a semantic middleware showed in Fig. 5.

AgOnt: Ontology for Agriculture Internet of Things

Middleware

Reasoning Server

135

Query Interface

AgOnt Ontology Models

Fig. 5. Reasoning on the knowledge base

A user can query the abnormal condition history of a product after the knowledge base is built based on the ontology. The knowledge base has two kinds of descriptions. One is the classification of the agriculture terminologies and their relationship, the users and machines can reason and analyze the structure association of agriculture concepts. The other is the concrete instances of the products, their conditions and relationship, where the users and machines can judge what the history condition caused an unsatisfied product at the end. For example, assume at some phase a decayed product ABP is found, the user wish to identify where the problem comes from. Depending on the setting of abnormal condition, the user can query the history to find which phase may cause it. Assume the abnormal condition is “the temperature > 4 degC and the humidity > 30%”, following query procedure is built to identify the problem. Set a search depth upper bound H; Searchdepth=1; CurProduct=ABP; do { Select x from products where CurProduct source-from x and x has-Property temperature > 4 degC or x has-Property humidity > 30%; If x is not empty, return x; else { CurProduct =x; searchdepth ++;} } while (searchdepth < H)

4 Related Work In the agricultural sector there exist already many well-established controlled vocabularies, such as FAO's AGROVOC Thesaurus [8]. However, to build a semantic tool entirely effective on the Internet, there is a need to re-assess the traditional "thesaurus" approach and move towards to the development of "ontologies". Taking FAO's multilingual thesaurus AGROVOC as a starting point, the AOS Concept Server [9] is a project for such purpose with helping structuring and standardizing agricultural terminology to be used in a wide range of systems in the agricultural domain. The Concept Server will provide a core ontology in the domain of agriculture

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that people can take as a starting point for building more detailed domain specific ontologies. Although it is a really huge data store in agriculture, the server is more informatics-oriented than food lifecycles tracking. Xie et al. [10] presented an agriculture-specific ontology to meet the requirement of agricultural knowledge processing and discussed the method for agricultural knowledge acquisition and representation. Gangemi et al. [11] aimed at building an ontology in the fishery domain through the conceptual integration and merging of existing fishery terminologies, thesauri, reference tables, and topic trees. The ontology will support semantic interoperability among existing fishery information systems and will enhance information extraction and text marking, envisaging a fishery semantic web. In summary, above ontologies are not helpful from the view of agriculture IOT application, where more semantic interoperability between grain or food lifecycles is focused. It is necessary to create a specific ontology suitable for the integration of multiple data sources of multiple phases so that all history record can be recalled.

5 Conclusion and Future Work The AgOnt ontology gives a description of the concepts of agriculture terms and lifecycle between seeds, grains, transportations, storage and consumption. According to the classified domain terms and definition based on attribute descriptions in the ontology, a semantic middleware can reason on the food healthy knowledge. By using this proposed approach, the distributed agriculture products’ information system can be accessed seamlessly. Thus follow-up users can know the history of the product before making a further processing. And the mechanism can also be used to recall when and where may take responsibility when a problem is found. The future work is to find mechanisms of managing the agriculture ontology at runtime, to build a user query model to map various user requirements to reasoning procedures, and to sum up the experience of migration from the legacy systems to an ontology-based system.

Acknowledgement Project supported by the National High Technology Research and Development Program of China (2008AA01Z208 and 2009AA01Z405), the National Natural Science Foundation of China (60772150), and the Youth Foundation of Sichuan Province (2009-28-419) and the Applied Basic Research Program of Sichuan Province (2010JY0013).

References 1. Katasonov, A., Kaykova, O., Khriyenko, O., Nikitin, S., Terziyan, V.: Smart Semantic Middleware for The Internet Of Things. In: 5th International Conference on Informatics in Control Automation and Robotics, pp. 169–178. INSTICC, Madeira, Portugal (2008)

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2. Yan, L., Zhang, Y., Yang, L.T.: The Internet of Things: from RFID to the Next-Generation Pervasive Networked Systems. Auerbach Publications, FL (2008) 3. Brock, D., Schuster, E.: On the Semantic Web of Things. In: Semantic Days 2006, Stavanger, Norway (2006) 4. Henson, C., Neuhaus, H., Sheth, A., Thirunarayan, K., Buyya, R.: An Ontological Representation of Time Series Observations on the Semantic Sensor Web. In: 1st International Workshop on the Semantic Sensor Web, Herkalion, Greece, pp. 79–94 (2009) 5. Kuhn, W.: A Functional Ontology of Observation and Measurement. In: Janowicz, K., Raubal, M., Levashkin, S. (eds.) GeoS 2009. LNCS, vol. 5892, pp. 26–43. Springer, Heidelberg (2009) 6. Fensel, D., Lausen, H., Polleres, A., de Bruijn, J., Stollberg, M., Roman, D., Domingue, J.: Enabling Semantic Web Services. Springer, Heidelberg (2007) 7. Niles, I., Pease, A.: Towards a Standard Upper Ontology. In: 2nd International Conference on Formal Ontology in Information Systems, pp. 2–9. ACM Press, New York (2001) 8. AGROVOC Thesaurus, http://www.fao.org/agrovoc 9. AGROVOC Concept Server Workbench, http://naist.cpe.ku.ac.th/agrovoc 10. Xie, N.F., Wang, W.S., Yang, Y.: Ontology-based Agricultural Knowledge Acquisition and Application. In: 2nd IFIP International Conference Computer and Computing Technologies in Agriculture, vol. 1, pp. 349–357. Springer, Heidelberg (2008) 11. Gangemi, A., Fisseha, F., Pettman, I., Pisanelli, D.M., Taconet, M., Keizer, J.: A Formal Ontological Framework for Semantic Interoperability in the Fishery Domain. In: ECAI 2002 Workshop on Ontologies and Semantic Interoperability, pp. 16–30. IOS Press, Amsterdam (2002)

Auto Recognition of Navigation Path for Harvest Robot Based on Machine Vision Bei He1, Gang Liu1, Ying Ji1,2, Yongsheng Si1,2, and Rui Gao1 1

Key laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing, 100083, China 2 College of Information Science & Technology, Agricultural University of Hebei, Baoding 071001, China [email protected]

Abstract. An algorithm of generating navigation path in orchard for harvesting robot based on machine vision was presented. According to the features of orchard images, a horizontal projection method was adopted to dynamically recognize the main trunks area. Border crossing points between the tree and the earth were detected by scanning the trunks areas, and these points were divided into two clusters on both sides. Resorting to least-square fitting, two border lines were extracted. The central clusters were gained by the two lines and this straight line was regarded as the navigation path.Matlab simulation result shows that the algorithm could effectively extract navigation path in complex orchard environment, and correct recognition rate was 91.7%. The method is proved to be stable and reliable, and with the deviation rate of simulation navigation angle compared with the artificial recognition angle is around 2%. Keywords: Navigation path, Machine vision, Orchard environment, Image segmentation, Least square-fitting.

1 Introduction As a type of agricultural robot, fruit-picking robot has great potential application prospect. Picking robot technology mainly includes three aspects: recognition, picking and movement[1]. Recognition contains recognition of ripe fruits, and acquiring the location of fruits; picking mainly includes the design of the mechanical arms and motion control; movement mainly refers to robot navigation. At present, the recognition has been studied and researched by many research institutions and has become a relatively mature method to many varieties of fruits and vegetables like apples, oranges, cucumbers etc[2-6]. However, the research about robot navigation based on open environment of orchard is rare on report[7]. As the development of automation, generally used navigation sensors include global positioning system (GPS), vision sensor, ultrasonic wave sensor, laser scanner and geomagnetic direction sensor at present[8-9]. Current research mainly focuses on two promising methods, machine vision and GPS navigation. Most of these studies of automatic guidance systems dealt with spatial positioning-sensing systems and steering control systems for following a predetermined path[10]. And there are only a few D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 138–148, 2011. © IFIP International Federation for Information Processing 2011

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researches on the field of the orchard navigation robots. Among them, applying machine vision to orchard navigation has lots of advantages, and can effectively solve the problems in autonomous navigation of agricultural robot as explained below. First of all, it does not require a specific navigation aid. Secondly, machine vision can adapt complex environment, including complex terrain, unknown and variable environment parameter, etc. Lastly, more flexible visual field, integrate information and high reliability and accuracy will be used. The robot can move autonomously more effectively with vision technique applied to the navigation of harvesting robot. The research of the paper is mainly about traveling device, vision system, Arm & gripper of the apple picking robot. Machine vision system is to recognize and locate fruits and the navigation system is to provide the moving route shown as the Figure 1. This paper presents a method which adopts machine vision to acquire orchard images which are studied to obtain the navigation route to achieve robot visual navigation.

Fig. 1. Components of the robot

2 Materials and Methods 2.1 Characteristics of the Orchard Environment Image Orchard navigation, like farmland Navigation, can also use the ridge boundary line detection that makes the aerial view of the whole orchard as target to obtain the navigation route through its visual system so as to complete autonomous navigation operation[11]. Actually, the orchard environment is complex with its non-structural characteristics and diverse background. Besides, the orchard is affected by natural sunshine, temperature and other natural aspects. Different size of fruit trees and varied growth patterns make the orchard more uncertain, which raises the real-time requirement of orchard navigation. The plants of early crops in the farmland such as wheat, soybeans, etc. are relatively short and are cultivated neatly by row. Each row is parallel to another[12]. Meanwhile, the crops are usually green, the crop rows are consecutive and in line shape or small curvature and the navigation characteristics detected do not mutate

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within a short time[13].However, fruit plants have different height, complex levels and random spatial arrangements, the vision system can hardly detect the obvious and consecutive navigation characteristics and cannot use the algorithm of current farmland visual navigation system directly. The plants of standard fruit trees are really tall with a trunk height of 70-80cm as usual, which makes it more obvious to make a distinction between trunk and background in visual. Based on these characteristics of standard fruit trees, the intersection points of trunks and the ground can be found after highlighting the main trunks. And these points can be used to generate the navigation path. 2.2 Image Acquisition and Laboratory Equipment The Images used in the experiments are collected from the apple orchard in Nanlang village, Qinshui County, Shanxi province and taolin village, Changping District Beijing. The resolution of the collected images is 640 × 480. The image processing computer is configured to be 2.2GHz frequency, 1.25G memory. The simulation platform is Matlab R2009a 2.3 Identification of the Main Area At present, most apple trees are planted in dense manner, but different standard trees have different growth patterns. The apple trees in Nanlang village, Shanxi province are the mixture of both Gala and Fushi, which have similar characteristic with Qiaohua tree whose trunk is about 70-80cm high. Based on this characteristic, the main area of apple tree can be made more visible through the color characteristics of image segmentation. Through the analysis of profile control line graph shown as follows, a suitable partition factor can be found to study the color characteristics of trunks and the background area. As shown in the Figure 2.

Fig. 2. Result of line profile map


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As to the study of line L, R and B pixels are the components of their gray value respectively. The yellow curve represents R-B value. It is easy to find that there is little difference between Red R and Blue B of the trunk area, while R and B of the soil differ a lot and green component of the leaves is a little bit more obvious. R-B can separate the trunk area and the orchard background effectively. It can be seen from the yellow curve that the R-B components of the trunk are in a peak region, while the R-B components of the background area are in the much falter region. The little noise produced in the process of orchard pruning trimming where the branches fell on the ground will be eliminated in later algorithm. Having the original image been transformed into gray level by using the R-B color factors, the optimal segmentation threshold value can be obtained by two-dimensional OTSU algorithm[14]. And then get the gray image diarized, so as to acquire the requested information in the trunk area. The algorithm not only uses the intensity distribution information of the points but also consider the relevant pixel space information among the points, which makes it better than one-dimensional OTSU segmentation algorithm.

(a) (a) Original image

(b) (b) Gray image

(c) (c) two-dimensional OTSU

Fig. 3. Binary image of the orchard

There will be a small amount of dry branches, weeds, etc., on the ground, which can be regarded as noise. The small branches are also easy to produce noise. Before accessing to the trunk region, morphological image processing is needed to avoid effects which noise has on the extraction of trunk’s characteristics. The above images are corroded and dilated by 3*1 respectively. The corrosiveness is to remove the effects of small and dry branches, the dilation in the growth direction of trunk is to eliminate empty. After this process, there will still be some noise left, which should be removed to avoid the misunderstanding of the extraction of future characteristics. First of all, give the morphology image area mark, and calculate the area of each region, then remove the area of land that is less than 1/15 of the largest area, finally we can get the binary image shown as Figure 3 and 4(a).

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(a)

(b)

(a) Image by post-processing

(b) Distribution diagram of each line horizontal projection

Fig. 4. The main trunk area detection by horizontal projection

From the binary image in Figure 4(a), it can be seen that the intersection of the main trunk and the ground are concentrated in the lower part of the image, the further the main trunk area is, the smaller the image is. The upper part of the image is small branches, sky, and so on. In order to highlight the main trunk area, the local characteristics of fruit trees can be neglected, and the horizontal projection method is used to extract the main trunk area. The steps are as follows: Set image resolution to M × N , I (i, j ) as the image gray value point ( i , j ) , then scan the binary image progressively, and calculate the horizontal projection value of each line s (i ) , s (i ) =

M

∑

I (i , j )

( i = 1, 2 , L , N )

j =1

which is to select the appropriate threshold, if it is appropriate, it is the main trunk area. Otherwise, it is not. From Figure 4(b), it is easy to see that the horizontal line value in the central area is really low. That is the line where the trunk, small branches and the sky separate from each other. In actual practice, first set threshold T = 200 , record the row number and keep the pixel value below the line when the horizontal line number is smaller than the threshold value to extract the trunk area. In the image processing, extract main parts of the image area if the horizontal projection value is smaller than the threshold, the image resolution in the follow-up process is M × h. 2.4 Main Feature Point Extraction According to the features of standard tree that the trunk are upright and obviously easy to distinguish, the intersection of the trunk and the ground can be regarded as


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feature points to represent fruit trees, to reduce calculation. As the impact of small branches, a very small amount of noise is still existed in the picture after trunk extraction. By using the area threshold method, an area of less than the maximum area of 1/80 of the region is removed to get a binary image whose trunk area is clear as Figure 5(a) shows. Feature point extraction algorithm is described as follows: 1. Set an empty matrix P, the size is the same as the size of the trunk extraction regional image, marked as M × h . 2. Mark regionally the images whose noise has been removed. Scan each region which represents each fruit tree that has trunk feature. Suppose that there are n regions exist in this image. 3. Scan the marked region k line by line. Setting the current row is row i , tested them one by one by order of the columns. If the pixel value of the current detection point ( i , j ) is k, meanwhile, the meeting point ( i , j − 1) of the pixel values and point ( i + 1, j ) values are both 0, the tested point is suit to the features that the intersection of the trunk and the ground.(Referred as the candidate point). 4. Put the coordinates of the suitable candidate points in region k into the empty matrix P. Test again. Search the point has the largest abscissa value, which means finding the point that is closest to the ground to represent the fruit trees. Set the remaining points as background points. 5. If the detecting area k = n , stop searching. Otherwise, return (3). 6. After scanning the feature points by the above steps, each region will have a unique feature point to represent the fruit tree. By comparing 60 apple pictures which were taken in similar position, different time, different parts, fruit trees--the intersection of the trunk and the ground distribute on both sides of the image according to the probability. Therefore, when the feature points are classified, firstly the vertical midline of the image (half of the total number of columns the image) is taken as the base line of the feature points to classify. When the feature points in the image are on the left, the corresponding coordinates will be saved into array Q1; otherwise, when the feature point in the image are on the right, the corresponding coordinate values will be saved into array Q2. 2.5 Navigation Path Line Detection Similar to crops, fruit trees are naturally formed in a straight line. Similarly, the path of mobile robot in a short time can be approximately seen as a straight line. So the straight-line path model can be used on the study [13].The most generally used line detection methods are the least square method and the Hough transform and some methods based on these forms. This paper takes the intersection points of fruit trees and the ground as the feature points. Feature points in the vision field are limited, so the least square method which has high speed and accuracy, are adopted to test the two junction lines. Finally the robot's navigation path is generated by extracting the center points of the junction line.

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(a)

(b)

(c)

(a) The main trunk area (b) Feature points (c) Navigation line detection Fig. 5. Guidance path line of an apple orchard (Qinshui, Shanxi)

3 Results and Analysis The angle between geometric central line which serves as navigation line and horizontal line is an important factor of navigation. It decides the angle that the robot needs to adjust. It means the robot is walking along the best direction of safe moving in visual field, when the angle is close to 90 °[11]. 60 images were used to test the algorithm. 20 images are taken in orchards in Nanlang Village, Qinshui County, Shanxi province, and the rest are taken in orchards in Taolin Village, Changping District, Beijing. Correct recognition rate was 91.7%. Table 1. Comparison of navigation lines in different orchards

The

Weather

Figure

Segmente

orchard

Condition

number

d result

The number of

The

extracted feature points

simulation navigation

Left line

Right line angle /(°)

Taolin,

Sunny

Effective Fig.6

Beijing

(Front lit)

Qinshui,

Sunny (Front lit)

114.2°

5

6

82.5°

5

5

95.8°

Fig.3(c)

Qinshui,

Effective Cloudy

Shanxi

3

Effective Fig.5

Shanxi

3 Fig.6(b)

Fig.7 Fig.7(b)


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Table 1 shows the segmentation, extracted feature points and simulation angles generated by this algorithm under different orchard environment and the automatic extraction of navigation lines in the two different orchard backgrounds. It can be seen from the table, the navigation line could be auto generated in various orchards environment. The angles of the simulation navigation lines are nearly 90°and can fulfill the path extracting request of autonomous navigation in complex orchard environment.

(a)

(b)

(c)

(d)

(e) (f) (a) Original Image (b) Segmentation Image (c) Image by post-processing (d) The main trunk area (e) Feature points (f) Navigation line detection Fig. 6. Guidance path line of an apple orchard ( Taolin, Beijing)

Five images which are failure to extract the navigation line are all taken in Taolin Village, Changping District of Beijing. Firstly the main failure reason is the complexity of background. And there are the iron rods next to fruit trees, which can cause the adjacent segmentation of trunk regions, reduce the feature points, result in detection errors. Secondly, because of the light effect, there are many shaded area in trees, resulting in the similar color of dry twigs and leaves, causing false segmentation. To verify the reliability of the algorithm, the simulation result was compared with manual recognition. Four images were taken from each orchard. Calculate the horizontal level of artificial fitting navigation under Matlab to get the deviation between the artificial recognition angle and simulation navigation angle. (shown as Table 2), and the deviation turns out to be around 2%.

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(a)

(b)

(c)

(d)

(e) (f) (a) Original Image (b) Segmentation Image (c) Image by post-processing (d) The main trunk area (e) Feature points (f) Navigation line detection Fig. 7. Guidance path line of an apple orchard ( Xinshui, Shanxi)

Table 2. Comparison between simulation and artificial recognition

Simulation

Artificial

Devia-

navigation

recognition

tion

angle /(°)

angle /(°)

/(°)

1

113.2

110.6

2.6

2.35

Taolin, Beijing

2

106.9

103.6

3.3

3.19

(Fig.6 etc)

3

94.1

97.2

3.1

3.19

4

80.7

83.8

3.1

3.70

The orchard Environment

Number

Deviation rate /%

5

82.5

83.9

0.6

0.72

Qinshui, Shanxi

6

86.1

85.7

0.3

0.35

(Fig.7 etc)

7

93.8

92..6

1.2

1.30

8

87.5

86.8

0.7

0.81


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4 Conclusions (1) The color difference R-B and two-dimensional OTSU algorithm was employed to segment the trunk from the background. Dead leaves and soil background did not affect the segmentation of the trunk region. But the algorithm is not effective when processing green weeds on the ground. Morphological method was adopted to eliminate the noises such as tiny branches and fading leaves, horizontal projection method was adopted to dynamically recognize the tree trunks, and the region segmentation was used to eliminate the influence of tiny branches for a second time. This algorithm can extract the main trunk area effectively. (2) By scanning the trunks areas, border crossing points of the bottom of the tree and ground were detected, and these points were divided into two clusters on both sides based on neighboring relationship. Resorting to least-square fitting, two border lines were extracted. The central line was gained by the two lines. It is robust and effective in many orchard environments. The recognition rate is 91.6%. (3) The simulation result is compared with artificial recognition in two orchard environment. The result shows that the generated navigation path is reliable, safe and can satisfy the moving request of harvesting robot. (4) This algorithm is suitable for the orchard where the ground had less weed and the main area of standard trees were more visible. For the stunted trees, if there are more weeds and the background is extremely complex in an orchard, it is better to improve the algorithm or use another method. Acknowledgments. This research is sponsored by the project 2006AA10Z255 and National Natural Science Foundation of China (Grant No.30900869). All of the mentioned support is gratefully acknowledged.

References 1. Kitamura, S., Oka, K.: Recognition and Cutting System of Sweet Pepper for Picking Robot in Greenhouse Horticulture. In: Proceeding of the IEEE International Conference on Mechatronics & Automation Niagara Falls, Canada, pp. 1807–1812 (2005) 2. Tarrio, P., Bernardos, A.M., Casar, J.R., Besada, A.: A Harvesting Robot for small Fruit in Bunches Based on 3-D Stereoscopic Vision. In: 4th World Congress Conference on Computers in Agriculture and Natural Resources, USA (2006) 3. Kondo, N., Yamamoto, K., Yata, K., Kurita, M.: A Machine Vision for Tomato Cluster Harvesting Robot. In: ASABE Annual International Meeting, Rhode Island (2008) 4. Fangming, Z., Naiqian, Z.: Applying Joint Transform Correlator in Tomato Recognition. In: ASABE Annual International Meeting, Rhode Island, pp. 1–9 (2008) 5. Hannan, M.W., Burks, T.F., Bulanon, D.M.: A Real-time Machine Vision Algorithm for Robotic Citrus Harvesting. In: ASABE Annual International Meeting (2007) 6. Bulanon, D.M., Kataoka, T., Ota, Y.: A Segmentation Algorithm for the Automatic Recognition of Fuji Apples at Harvest. Biosystems Engineering 83(4), 405–412 (2002) 7. Wilson, J.N.: Guidance of Agricultural vehicles-a historical perspective: Computers and Electronics in Agriculture, Canada (2000) 8. Kondo, N., Monta, M., Noguchi, N.: Agri-Robot(I)-Fundamentals and Theory. Corona Publishing Co., Ltd. (2004)

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9. Keicher, R., Seufert, H.: Automatic guidance for agricultural vehicles in Europe. Computers and Electronics in Agriculture 25(12), 169–194 (2000) 10. Noguchi, N., Ishii, K., Terao, H.: Development of an agricultural mobile robot using a geomagnetic direction sensor and image sensors. Journal of Agricultural Engineering 67, 1–15 (1997) 11. Jiayi, W., Qinghua, Y., Guanjun, B., Feng, G.: Algorithm of Path Navigation Line for Robot in Forestry Environment Based on Machine Vision. Transactions of the Chinese Society for Agricultural Machinery 40(7), 176–179 (2009) 12. Gang, S., et al.: Research Advance in Machine Vision Guidance of Agricultural Vehicles. Journal of Anhui. Agr. Sci. 35(14), 4394–4396 (2007) 13. Qian, C., Ku, W.: Vision Navigation Based on Agricultural Non-structural Characteristic. Transactions of the Chinese Society for Agricultural Machinery 40(add), 187–190 (2009) 14. Xiaojun, J., Anni, C., Jingao, S.: Image segmentation based on 2D maximum betweencluster variance. Journal of China Institute of Communication 22(4), 71–76 (2001) 15. Weimin, Y., Tianshi, L., Hongsh, J.: Simulation and experiment of machine visionguidance of agriculture vehicles. Transactions of the Chinese Society of Agricultural Engineering 20(1), 160–165 (2004) 16. Qinghua, Y., Jiayi, W., Guanjun, B., Feng, G.: Algorithms of Path Guidance Line Based on Computer Vision and Their Applications in Agriculture and Forestry Environment. Transactions of the Chinese Society for Agricultural Machinery 40(3), 147–151 (2009) 17. Guoquan, J., Xing, K., Shangfeng, D.: Detection algorithm of crop rows based on machine vision and randomized method. Transactions of the Chinese Society for Agricultural Machinery 39(11), 85–88 (2008)

An Agricultural Tri-dimensional Pollution Data Management Platform Based on DNDC Model Lihua Jiang1,2, Wensheng Wang1,2, Xiaorong Yang1,2, Nengfu Xie1,2, and Youping Cheng3 1

Agriculture Information Institute, Chinese Academy of Agriculture Sciences, Beijing, 100081, China 2 Key Laboratory of Digital Agricultural Early-warning Technology, Agriculture Information Institute, Chinese Academy of Agriculture Sciences, Beijing, 100081 3 Agriculture Bureau, Huailai County, Hebei Province, 075400, China {jianglh,wangwsh,yxr,nf.xie,youping}@caas.net.cn

Abstract. DNDC is a computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems. It is used in agricultural tri-dimensional pollution control widely. Learning from abroad advanced technologies and research methods, we have developed an agricultural tri-dimensional pollution data submission and management platform based on DNDC model. The platform is very important for sharing and building our agricultural carbon and nitrogen chain database. Keywords: DNDC, United Storage, Format Conversion.

1 Introduction DNDC (DeNitrification-DeComposition) is a computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems. The model can be used for predicting crop growth, soil temperature and moisture regimes, soil carbon dynamics, nitrogen leaching, and emissions of trace gases including nitrous oxide (N2O), nitric oxide (NO), dinitrogen (N2), ammonia (NH3), methane (CH4) and carbon dioxide (CO2). Studying on carbon and nitrogen chain in agricultural tri-dimensional pollution [1] and blocking carbon and nitrogen pollution sources is very important for prevention and treatment agricultural carbon and nitrogen pollution [2]. Development DNDC [3] model needed data management system can undoubtedly provides important scientific support for our country agriculture carbon and nitrogen emission reduction [4] database construction and data sharing. Study and develop a data submission, management and supporting DNDC model platform for every level user in agriculture tri-dimensional [5] preventive treatment centre. Data submission and management module provides data management interface for administrators and is in favor of adjusting agriculture tri-dimensional pollution data. Assistant DNDC [6] model module can read out data from DNDC database for users and create txt files and also imports DNDC model results to database according to user’ willing. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 149–154, 2011. © IFIP International Federation for Information Processing 2011

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2 Platform Main Functions This platform is made up of distributed data submission and management module, submitted data centralized management module and assistant DNDC module. The functions of platform are shown in figure 1.

platform

Data Submission System

Central Administrator

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personal Management

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Base Administrator

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Connect Database

Base Operation

Create txt file

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GIS File

lab_clim File

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Base Info Modify

Region Management

Change Apply

Apply Feedback

Property Selection

Table Selection

Batching Submission

Submit Item by Item

Base management

Personal Info Modify

Property Apply

Table management

Property Management

Data management

Fig. 1. Platform main functions

2.1 Distributed Data Submission and Management Module Distributed data submission and management module provides several function interfaces for users and base administrator can realize heterogeneous data submission. Base administrator can submit the collected agricultural tri-dimensional data to central database by using function interface in favor of centralized and unified management. Central administrator can manage all base submitted information including modifying and deleting the information. In addition, base administrator can self-determine the submitted tables and corresponding attributes of tables and make up personal operation interface. The platform realizes personal management. 2.2 Submitted Data Centralized Management Platform can realize central management of submitted data from bases and provides functional interface in which central administrator can modify construction of database directly. It is very convenient and visual for users to operate database. The operation of database construction includes adding and deleting tables and adding and deleting attributes of tables.

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2.3 Assistant DNDC Module Functions of the module is shown in figure 2 including database connecting, data format conversion from database to DNDC model, storage of results from DNDC stimulation model to database . DNDC model sometimes will use some text files for example climate files, fertigation files, flooding files in site mode and initial data files in region mode and these files can created in assistant DNDC module. User can read result files from DNDC model to database in the platform. Within the power of privilege, user can select different formats result files in different modes and store them in corresponding database. Txt batch data

Txt format data

read in data DNDC model read out results calculate software

Csv format data

Input data item by item

acquire data

Database

build up platform, read out data from database, convert format, acquire data and create DNDC model recognized documents

read in

build up platform, read out result file and convert to database recognize format.

Fig. 2. Main functions logic diagram of assistant DNDC module

3 Key Technologies In developing process, we solve the key technology problems including distributed heterogeneous data submission, data submission item by item or in batch, format conversion from database to DNDC model, storage of DNDC model results, function management mechanism based on role and so on. 3.1 Distributed Heterogeneous Data Submission Technology In distributed data submission process, the tables submitted by different bases are different. If in designing process, every base is shown all the tables, the system is not well targeted and not convenient for users. But if every base corresponding table is designed in advance, it is also not perfect, because it is possible to change the submitted data tables. The fields in tables are in the same reasons. Aiming at above problems, the platform provides table views and user personal control of submission interface fields display function. At first, users can select needed tables by random in platform and then navigation bar will embody users’ personal choice and provide pointed interface. In the next

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place, users can select corresponding field attributes of tables in function interface and make up personal submission interface. It is convenient for different users to operate. 3.2

Data Submission Item by Item or in Batch Technology

Distributed data submission and management module mainly realizes different bases and different information submission function. So submission function is the key and difficult problem of the platform. Because the quantity of bases using the platform is not limited and data bulk submitted by bases is not limited, if system only provides submission item by item function, when submitted data bulk is so large, system will waste a lot of time. So system also provides submission in batch function. In submission data item by item, formats of submitted data must be controlled seriously when base administrator submits data to central databases. In submission item by item interface, platform provides description of submitted attributes data format and clear clue of input error. User can realize submit a small quantity of unpacked data correctly. When user needs to submit a mass of packed data, data submitting in batch module is needed and shows superiority fully. The system provides two different batching submission means: Import from Excel files to database. If user has already stored collected data in definite format in Excel files and can use the batching submission interface to import them to central database quickly after mapping. It can save a lot of importing item by item time. Import data from local database to central database. In many cases, user stores collected data in local database in favor of partial management. At the moment, user can use batching submission module to copy local data to central database quickly.

①

②

3.3

Format Conversion Technology from Database to DNDC Model

The central database stores data from every base. Administrator stores them in fixed format to many stables in database. When DNDC model is used to stimulate agricultural tri-dimensional pollution preventive treatment, data in database is needed and a lot of them need to be adopted in fixed text document format. Formats of the data in database should be converted to format which DNDC model can recognize and use. Format conversion is a dynamic form. Platform shows all choices possibly used in text files, every table name relevant to DNDC model in database and all fields name of the tables. When user uses the platform, he can decide items in text file and the table and fields in database. 3.4

Storage DNDC Model Results to Database Technology

User uses DNDC model to stimulate and get some results. According to different stimulation in different modes, the result files have two formats: txt and csv. The format of result data in above two formats result files is the same and assistant DNDC module can read out and show data items of result files. If user wants read data item in result files in some table in database, he can dynamically select data item in choice box. In the process of reading in, user can decide how to map data items into the fields in tables or whether read data items to correspond tables in database. And txt result file data can be

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read in five tables in the database and user can decide whether read in tables or which tables should be read in. Csv result file data can be read in one table in database. When user selects fields corresponding to data item, he can import all data to database. When the above two types of files are imported to database, the user’ name is stored in database automatically in order that central administrator can adjust database management. 3.5

System Function Management Mechanism Based on Role

The system has three user roles: central administrator, base administrator and DNDC user. Base administrator collects local base data and submits to platform. Central administrator has all management privilege and can limit base administrator’ management privilege. When DNDC user uses DNDC model, central administrator will give privilege to use assistant DNDC model. DNDC user can be base administrator and central administrator and also other privileged user.

4 Technology Innovation 4.1 Distributional Heterogeneous Agricultural Tri-dimensional Pollution Data Submission Technology The platform solves distributed heterogeneous agricultural tri-dimensional pollution data submission in many nodes technology and provides table views and personal control of submission interface fields display function. At first, user can select needed tables at random in the platform and navigation bar will embody user’ personal selection and provides pointed interface. Secondly, user can select corresponding field attributes of tables and create personal submission interface. It is convenient for different users to operate different operations. Platform provides data submission item by item and batching submission. In submission data item by item interface, formats of submitted data must be controlled seriously when base administrator submits data to central databases. Platform provides description of submitted attributes data format and clear clue of input error. User can realize submit a small quantity of unpacked data correctly. Batching submission: Platform provides two different batching submission methods: importing from Excel files to central database and importing from local database to central database. By the batching submission module provided by system, users can copy data in local database quickly to central database.

①

②

4.2 Mutual Access Technology of DNDC Model Software and Database In the process of DNDC model accumulation software working, users should input relevant data and read in some fixed format text files under some circumstances. All data should be read out from database, so DNDC software is needed to be able to visit database and can read the data. The result data stimulated by DNDC model should be stored in database and DNDC software is required to mutual access with database. Assistant DNDC module can realize DNDC and database mutual access and data transmission. When data is read from database and built up txt files, the platform not

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only shows txt files but also tables and fields in database. So user can decide how to map by himself. When part of result data from DNDC model is stored in database, result files has different formats and store in different formats. After user selects the loading result files, platform will show every data item and field names of tables in database. User can decide how to map data item in result files to fields of tables in the database and read in result data to database. It is convenient to user.

5 Conclusion Combined with our country ecology system actual situation, introducing foreign developed DNDC model and development carbon and nitrogen emission reduction motivation model can help scientists and engineers judge intuitively and make a strategic decision. So we build up a mutual platform in which collect and manage data for DNDC model. For studying agricultural tri-dimension pollution, validation, amendment and operation agricultural tri-dimension pollution carbon and nitrogen emission reduction motivation model needs a lot of data support, so standardization of database is very important. In the platform, database can receive all the collected data from bases administrator. Central system administrator can adjust database according to types of collected data in order to supporting our country actual situation and DNDC model.

Acknowledgements This work is supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), Special fund project for Basic Science Research Business Fee, AIIS(Grant No. 2010-J).

References 1.

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3. 4. 5. 6.

Zhang, L.J., Zhu, L.Z.: Agriculture tri-dimensional pollution preventive treatment is strategy requirement in present environment protection. Environmental Protection 05, 36–43 (2007) Qiu, J.J., Wang, L.G., Li, H., Tang, H.J., Li, C.S., Van Ranst, E.: Modeling the impacts of soil organic carbon content of croplands on crop yields in Chin. Agricultural Sciences in China 8(4), 464–471 (2009) Chen, P.Q., Huang, Y., Yu, G.R.: Carbon cycle of terrestrial ecosystem. Science Press, Beijing (2004) Li, H., Wang, L.G., Qiu, J.J.: Aoolication of DNDC model in estimating cropland nitrate leaching. Chinese Journal of Applied Ecology 20(7), 1591–1596 (2009) Huang, M.X., Zhang, S., Zhang, G.L.: Nitrate leaching from a winter wheat summarize rotation in Beijing area. Geographical Research 21(4), 425–433 (2002) Lu, X.Y., Cheng, G.W., Xiao, F.P., Fan, J.H.: Modeling effects of temperature and precipitation on carbon characteristics and GHGs emissions in Abies fabric forest of subalpine. Journal of Environmental Sciences 20, 339–346 (2008)

An Analysis on the Inter-annual Spatial and Temporal Variation of the Water Table Depth and Salinity in Hetao Irrigation District, Inner Mongolia, China Jun Du1,2, Peiling Yang1,*, Yunkai Li1, Shumei Ren1, Xianyue Li1, Yandong Xue1, Lingyan Wang1, and Wei Zhao1 1

College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing 100081, China 2 Bureau of Ningxia Farm, Yinchuan Ningxia 750001, China [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract. Long-term Yellow River irrigation and the unique natural conditions in the Heitao Irrigation District (HID) Inner Mongolia, China, has led to serious environmental problems such as the shallower groundwater table and soil secondary salinization, etc. The conflicts among socio-economic development, water shortage and environmental degradation have become increasingly critical. By using the statistical methods, geo-statistical methods and ArcGIS9.0, we analyze the temporal and spatial variation of depth to water table (DWT) and groundwater salinity in the three different irrigation seasons in 2001, 2002 and 2003 respectively. The results show that DWT and groundwater salinity has formed a ribbon distribution after the long-term Yellow River irrigation. DWT is medium spatial correlative and the average spatial autocorrelation distance is 18.5km; the groundwater salinity is strong spatial correlative and the average spatial autocorrelation distance is 12.5km. The inter-annual distribution of DWT and groundwater salinity in 2001 is quite similar with it in 2002 and 2003. The DWT in western area, eastern area and a small part of middle area are shallower than other area in HID. The average DWT in March reached maximum and its minimum is in November each year. There are two high salinity degree zones (M>5000mg/l and even some other M>30000mg/l). The shallower groundwater salinity in the southeast and northwest are higher than that of in the middle part of HID. The shallower water table depth is, the higher the salinity of groundwater will be; the deeper water table depth is, the lower the salinity of groundwater will be. Keywords: Hetao Irrigation District, Depth water table, groundwater salinity, spatial and temporal variation.

1 Introduction The HID (40°19′-41°18′ N, 106°20′-109°19′ E) is one of the three largest irrigation districts in China and be located the arid western part of Inner Mongolia Autonomous *



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Region, China (Fig.1). The total land area of the HID is about 1.1×104 km2, the irrigable land area is about 0.77×104 km2, but due to salinity problems, the currently irrigated land area is only about 0.57×104 km2. HID is in the mid-temperate zone with continental-monsoon arid climate. The weather is dry and hot during summer and severely cold with little snow in winter. From November to next March is a freeze-thaw period. Mean annual temperature is 6.3~7.7 . During the winter the average air temperature are -10 °C and the soil freezing depths about 1.0 m. The average annual pan evaporation is about 2164 mm. Across HID, the average annual precipitation 168 mm recently 10 years. The the average ground slope is about 1/8000~1/4000 (from southwest to northeast). The ground elevation ranges from 1043 m to 1018 m. The main soil types are irrigation-warping soil and saline soil which was the non-zonal soils in HID. The average soil bulk density (0-100cm) is 1.45 g/cm3 (Yang Jingyu, 2006).

℃

Fig. 1. Map of Irrigation District located and observation wells distribution

The most common crops are sunflower, wheat, and corn. Flood irrigation is the most common irrigation method in the HID. The average depth of irrigation is 450 mm. Farmland is typically irrigated 7 times each year in 3 irrigating seasons. The 3 irrigation seasons are: summer irrigation (3 irrigation times, from April to June), the first-autumn irrigation (3 irrigation times, from July to September), and the second-autumn irrigation (1 irrigation times, from October to November). The summer and first-autumn irrigation are during the growing season of the crop. The purpose of the second-autumn irrigation period is to “bank” soil water and leach salt. From 1989 to 2005, the average annual water diversion of irrigation from the Yellow River is 5.2×109 m3 by 1 main canal and 13 sub-main canals. Farmland recession water is drainage into the

An Analysis on the Inter-annual Spatial and Temporal Variation

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Wuliangsuhai Lake (Fig.1) by 1 main ditch and 10 sub-main ditches (Wang et al., 2004), and the average annual drainage amount was 0.5×109 m3. The water diversion of irrigation in 2001, 2002 and 2003 was 4.89×109 m3, 5.08×109 m3 and 4.1×109 m3 respectively. The salts which are brought by the irrigation water onto the farmland soil averages annually 235.5×108 kg, but the average annual discharged salt from the entire district by drainage is only 75.0×108 kg (Wang et al.,2004). For the period from 1987 to 1997, the average annual salt accumulation was estimated to be 3 mg/ha (Feng et al., 2003). About half of the irrigated cropland is saline-alkali soil (Feng et al., 2005). DWT is typically 1.0~1.5 m during the growing season and about 0.5 m following the second-autumn irrigation (from October to November) (Hao et al., 2008a). Due to the arid climate, the water diversion from Yellow River is critical for agriculture in the HID. There are significant negative effects from flood irrigation and canal seepage. The resulting shallower DWT combined with intensive evaporation has produced a very high threat of soil salinization. About 18.8% of the total land area in the irrigation district has been abandoned because of the salinization problem, and about 43.0% of the irrigated area is significantly impacted salinization problem of various degrees (Qu et al., 2007). Geological structure, geographical conditions, and climate factors determine the hydrological cycle and the resulting potential for salinization. In HID, there are many factors affecting the salinity such as precipitation, evaporation, DWT, and water diversion irrigation from Yellow River (Wang et al., 2007; Yue et al., 2009). The geological structure determines that the major hydrologic pathway for groundwater loss away from the HID is through the phreatic evaporation (Hao et al., 2008a). And the accumulated salt in the deeper soil were dragged into the surface soil in this process. So, the process of groundwater discharge is the dominated factors related to the production, development, and evolution of the soil salinization in HID. Irrigation (precipitation), infiltration, drainage, and groundwater evaporation, create the natural-artificial surface water system that is the most important factor in hydrological cycle for the HID (Hao et al., 2008a). A previous study showed that shallower groundwater had a significant effect on evaporation-transpiration and on soil water salinity. Evaporation exacerbated the surface soil water salinity, while the transpiration reduced the soil water salinity in the growth period of vegetation (Zhang et al., 2004). Climate condition and groundwater level fluctuation were the major environmental factors on the salinization of soil (Chen et al., 1997). Some studies on the salinity and DWT in HID showed that DWT is in 1.5m 2.0m contribute to the crops uptake the groundwater, but to minimize salinizion, DWT should be controlled below 2.0m from the soil surface (Kong, 2009). When the water diversions from the Yellow River to the HID were reduced by 30%, there was resulting higher of DWT and reduction in salinity soil, but there was greater potential for soil water deficit and crop water stress. (Qu et al., 2007). Other researchers have reported on the soil salinization issues in HID such as: saline land improvement, the relationship between the soil salinization and the DWT, the distribution of salt in the soil profile, the salt balance, and the water balance (Yang et a1.,2003; Kong, et a1.,2004;Wang et a1., 2004; Jia et a1.,2006; Gao et a1., 2008a,b). But there are very few studies on the distribution of DWT and groundwater salinity in the entire HID. Therefore, this report is an analysis of the spatial and temporal variation of the DWT and groundwater salinity.

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2 Materials and Methods 2.1 DWT Measurement and Water Samples Analysis In this analysis, the HID was divided into 5 irrigated areas: Yigan, Jiefangzha, Yongji, Yichang and Wulate (Fig.1). There were 178 wells (Fig.2) distributed over the entire District. Seventy-five wells were used to observe the DWT, 42 wells were used to collect groundwater samples, while 61 well were used for both DWT and water samples. measuring line

the well mouth

the ground surface Leaking hole

Bobber .1m ?0

non-woven Fabric Filter Layer

groundwater surface m Well shaft .25 R0

well bottom

Artesian Water

Fig. 2. The observation wells’ construction

The water quality analysis was performed at the Bayannur Water Conservancy-Science Institute Laboratory, Linhe, China. The constituent ions included: Na+ and K+ (determined by the flare photometer method), Ca2+ and Mg2+ (determined by EDTA titration), CO32- and HCO3- (determined by the acid titration), CL- (determined by AgNO3 titration method), SO42- (determined by EDTA indirect titration method). DWT was measured directly by a measuring tape with a detector at the end (Fig.2). The detector gave a signal when it reached the water surface and the length of the tape was recorded. Depth to groundwater table was calculated by subtracting the above ground wells’ body height (L1) from the recorded length of the tape. DWT calculate is given by: DWT=L-L1

(1)

Where DWT is the distance from the ground surface to the groundwater table. L is the distance from the well mouth to the groundwater surface. L1 is the distance from the well mouth to the ground surface. DWT were measured once a week. Water samples for chemical analysis were collected at 15 in every month.


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2.2 Typical Years and Typical Irrigation Period Data from 2001, 2002 and 2003 years were analyzed to determine the inter-annual variation of DWT and groundwater salinity, which will continue in time and reflects the water diversion for irrigation into the HID. Also in this analysis, the March, July, and November was taken as typical period which was the fluctuation of DWT and the variation of the shallower groundwater salinity. 2.3 Sampling Site Data and Processing The value of DWT and groundwater salinity of 178 observation wells were used to develop the point file with ArcGIS9.0 and project the coordinate transformation to produced the distribution map for the geo-statistical analysis (Fig.1).Then of DWT and groundwater salinity from corresponding sampling points were entered into Arc GIS9.0 to form the attributive data to matched the geographic data of sampling points. 2.4 Correlation Analysis SPSS13.0 was used to analyze the change and relationship between DWT and shallower groundwater in March, July and November respectively. The data of DWT from 7 wells which less affected by the groundwater exploration were selected to analyzed the annual change of DWT each year (Fig.4, 5, 6). The data of 61 wells’ DWT were as abscissa and with the corresponding salinity degree of groundwater as ordinate to make the relation curve to analyze the relationship between the groundwater salinity and DWT (Fig.25) in March, July, and November, respectively. 2.5 Geo-statistical Method and Processing Geo-statistical methods and ArcGIS9.0 were used to analyze the temporal and spatial variation of DWT and groundwater salinity from 2001 to 2003. Geo-statistical methods can be used to describe the spatial variability of environment and reveal the spatial heterogeneity and spatial pattern of natural phenomena (Pebesma et al. 1997). The semi-variogram model and Kriging interpolation are the two main geo-statistical methods used in this analysis (Jin et al.,1999; Sousa et al., 1999; Desbarats et al., 2002; Vijendra et al., 2004; Tong et al., 2007; Wang et al., 2007; Yue et al., 2009; Hu et al.2001, 2009; Husam,2010). To get a better spatial estimation from sampling points, the variance of estimation error should be minimal. The Kriging method was used to obtain the variance of estimate. The advantage of Kriging is that it is the Best Linear Unbiased Estimator of the unknown fields (Journel and Huijbregts, 1992).The Kriging variance of estimate is independent of the actual measurements from the field. Ordinary Kriging interpolation at a point x0 is given by:

，

n

Z ∗ ( x0 ) = ∑ nλi Z ( xi ) i =1

(2)

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Where Z*(x0) is the estimated value, n is the number of points, Z(xi) is the measured

value at point xi, and λi the Kriging weight. To calculate the Kriging variance, the semi-variogram is needed. The semi-variogram (usually called a variogram) is half the variance of measurement differences at all data pairs with the same distance (h). The Kriging variance is given by:

γ ( h) =

1 2Nh

Nh

∑ [Z ( x + h) − Z ( x )] i =1

2

i

i

（3)

Where r(h) is semi-variogram, h is step length, namely the spatial interval of sampling points used for the classification to decrease the individual number of spatial distance of various sampling point assemblages, N(h) is the logarithm of sampling point when the spacing is h, and z(xi) and z(xi+h) are the values when the variable Z is at the xi and xi+h positions, respectively.

Fig. 3. Experimental semi-variogram (dots) and fitted semi-variogram model

When computing the semi-variance (h) for different values of h, and when h is plotted versus (h), an experimental semi-variogram was obtained (Fig. 3. Sousa et al., 1999). However the experimental semi-variogram is not applicable in Kriging estimation because it cannot be represented by an equation. A semi-variogram model must then be adjusted to the experimental one, as exemplified in Fig.3 by the fitting of the Spherical equation. The best-fitted semi-variogram model has been used to produce the Kriging variance map. Selection of the best-fitted model was based on the condition that the root-mean-square was close to “0”,the average standard error is minimum, the mean standardized was close to the standard error and the root-mean-square standardized was close to “1” (Tang, 2007). Then GIS-Spatial Analyst tool in ArcGIS9.0 was used to produce a priority map and the best-fitted mode. To obtain Kriging variance, construction of the variogram is needed. The variogram parameters are the sill, nugget, and the range. The nugget is the variogram value at the origin. Sometimes the nugget is different from zero due to measurement error. The


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range is the distance at which the variogram reaches the sill value. Three modules included Spherical model (Eq.4), Exponential model (Eq.5) and Gaussian model (Eq.6) were used in this study. γ (h) = C0 + C1[1.5 ( h / a ) − 0.5 ( h / a ) ]

(4)

γ ( h) = C0 + C1[1 − e− h / a ]

(5)

γ ( h) = C0 + C1[1 − e− ( h / a ) ]

(6)

3

2

C0 is nugget, which represents the spatial heterogeneity of the stochastic component. The sill value, ( C0 + C ), is the attribute of the system or the maximum Where

variation of the regional variables. The higher the sill value is, the larger the degree of the total spatial heterogeneity will be. The value of a is the range. Sill (C0+C) and nugget (C0) were used to describe the spatial heterogeneity. The ratio of nugget: sill (C0/ C0+C) reflected the total spatial heterogeneity (Li et al., 1995). In this study, “Histogram” and “Normal QQplot” were the geo-statistical modules used with ArcGIS9.0. These modules were applied to analyze the normality of the 178 wells data of DWT and groundwater salinity each month. The results shown that the mean data comply with lognormal distribution. The ordinary Kriging interpolation method was applied to optimal mathematical model, and set the values of “Lag size” and “Number of lags” etc. to get the optimal predication map (Fig.7 24) and the best-fitted model (Eq.4 6). Table 1 2 lists several models with their respective sill and nugget.

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3 Results and Discussion 3.1 Analysis of the Spatial Structure of DWT and Groundwater Salinity

The ratio of C0: (C0+C) reflected the total spatial heterogeneity. A higher ratio indicates that the stochastic component was the main factor caused the spatial heterogeneity. The ratio of C0: (C0+ C1) was in the range of 25% 75% of the spatial structure of DWT in the three years(Tab. 1). It shown that the spatial structure variation of DWT was not only affected by the structure factors but also by the random factors (the stochastic component). Due to the average annual precipitation is 168 mm and the irrigation water is the mainly recharge source of DWT. So the spatial structure variation of DWT was affected by the time and amount of the agricultural irrigation mainly during the irrigation season. The structure factors such as the terrain, landform and climate would be responsible for the variation of the spatial structure of DWT when the total water diversions of irrigation were reduced. For example, although the water diversions of irrigation in 2003 were reduced to 80% of the average annual water diversions of irrigation and the ratio of C0: (C0+ C1) of the spatial structure of DWT in July and November decreased to 34.3 and 37.5 respectively, the distribution of DWT in July and November 2003 were similar to that of 2001 and 2002(Fig. 8, 11 and 14. Fig. 10, 12 and

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Table 1. The parameters of semi-variogram models for shallow groundwater table depth

Year

month

Ran

Nug

Par

Sill

C0/ (C0+ C1)

km

C0

C1

C0+ C1

%

Mar

Sph

22.7

0.15

0.11

0.26

58.2

July Nov

Exp Sph

11.5 13.4

0.10 0.24

0.20 0.09

0.30 0.33

51.8 73

March July

Sph Exp

28.4 19

0.04 0.05

0.01 0.06

0.05 0.11

72 46.6

Nov Mar

Sph Sph

19 22.7

0.16 0.03

0.14 0.02

0.29 0.05

53.6 52.7

Jul Nov

Gau Exp

19 10.7

0.04 0.14

0.08 0.24

0.13 0.38

34.3 37.5

18.5

0.09

0.09

0.19

47.8

2001

2002

Model

2003 mean

Table 2. The parameters of semi-variogram models for shallow groundwater salinity year

Ran

Nug

Par

Sill

km

C0

C1

C0+ C1

%

Gau

11.1

0.05

0.68

0.72

6.4

Jul

Sph

12.3

0.11

0.64

0.74

14.3

Nov

Gau

9.5

0.18

0.53

0.71

25

Mar

Sph

23

0.20

0.65

0.85

23.3

Jul

Sph

13.5

0.35

0.42

0.77

45.7

Nov

Gau

11.8

0.35

0.46

0.81

43.4

Mar

Exp

7.9

0.04

0.79

0.83

4.7

Jul

Exp

11.5

0.11

0.68

0.79

13.8

Nov

Exp

month

Model

Mar 2001

2002

2003

mean

C0/ (C0+ C1)

8.4

0.00

0.69

0.69

0.3

12.1

0.16

0.61

0.77

20.1

15.). Therefore, the spatial structure of DWT was the moderate spatial correlation and the average corresponding distance was 18.5 km among the three years (Tab.1). These result shown that the co-working of the structure factors and random factors were the mainly factor of the variation of the spatial structure of DWT in HID.


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The table 2 shown that the ratio of C0: (C0+ C1) of spatial structure of groundwater salinity varied from 0.3 to 45.7 in the three years. The spatial structure of groundwater salinity had the strong spatial correlation and the average corresponding distance was 12.1km. Since the salt which in the irrigation water (the Yellow River water salt content was 480 mg/l) were the mainly recharge resource of the shallower groundwater salinity, the different water diversions of irrigation would cause the variation of the shallower groundwater salinity. Meanwhile, the waste discharge of industries and civil life also enhanced the variation of it. This result shown that the groundwater salinity was affected by the random factors, such as irrigation water salt, field fertilization and waste discharge of industries and civil life in HID. 3.2 Analysis of Temporal and Spatial Variation of the DWT 3.2.1 Temporal Variation of DWT

The variation of DWT of 7 wells which without the affects of groundwater exploitation were similar among the three years (Fig 4, 5 and 6). Namely, the average DWT value was 2.0 m in January and reached the maximum value (2.5 m) in March, and then decreased to 1.5 m in May after the summer irrigation. Since the agriculture irrigation was gradually decreased from August to September, DWT increased to 2.2 m in September. Following the late autumn irrigation which the irrigation water amount were thirty percent of the total water diversion, the average DWT value rapidly decreased to 1.0 m in November. With the beginning of winter, the DWT gradually increased to 2.0 m by next January. Then, the fluctuation of the DWT completes the annual cycle. These peaks and valleys demonstrated that the time and amount of agricultural irrigation were responsible for the fluctuation of the DWT without the effect of irrigation exploitation in HID.

Fig. 4. Map of DWT variety, 2001

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3.2.2 Spatial Variation of DWT in Typical Period Owing to the temperature difference between the upper and under boundary of the freezing soil layer, the high absorption energy makes groundwater in air containing zone move towards the freezing layer so that the DWT value of 95% area of HID were in the ranged of 2 3 m in March each year (Fig.7, 10, 13).

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Fig. 7. Distributing map of DWT in March, 2001

Fig. 8. Distributing map of DWT in July, 2001

Because the water diversions of irrigation were reduced to 80% of the average annual water diversions of irrigation (52×109 m3) in 2003, and the irrigation water amount were reduced during the summer irrigation season. Therefore, the DWT value of 80% area of HID were in the range of 1.5 2.0 m, and the regions of DWT in the range of 2 3 m were continuously in July 2003(Fig.14). Although the DWT of 80% area of HID were also in the range of 1.5 2.0 m in July 2001, the regions of the DWT in the range of 2.0 3.0 m were scattered over the southwest in HID(Fig.11). In 2002, the total water diversions of irrigation were more than that of other two years. And the

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DWT value of 50% areas of HID were in the range of 1.0 1.5 m in 2002(Fig.8). It can be drawn into conclusion that the distributional-variation of the water diversions of irrigation among years caused the distributional-variation of DWT in the entire HID. In other word, the more the water diversions of irrigation were, the shallower DWT in HID would be. In additional, the figure 8, 11 and 14 shown that the shallower DWT region distributed in Yigan, southwestern part of Jiefangzha, minor area of Yongji and Wulate irrigation region. These regions were the agricultural areas with little in groundwater exploitation and lower terrain. While the deeper DWT regions distributed in the northeastern part of Jiefangzha, major part of Yongji and Yichang irrigation region. These regions were the agricultural areas with the higher terrain and cities-towns. And an amount of the groundwater exploitation were used to meets the demand of the domestic water, public facilities and urban greening in cities-towns.

Fig. 9. Distributing map of DWT in November, 2001






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Take 310#, 223# and 305# well as example, the average annual DWT were 3.7 m, 3.5 m and 3.5 m from 2001 to 2003, which were located around the city of Linhe, Wuyuan and Qianqi respectively. In second-autumn irrigation season, there was 1 irrigation-time in HID, and the irrigation amount was 30% of the total water diversions of irrigation. And the terrain slopes gently, the average ground slope is about 1/8000~1/4000 (from southwest to northeast). Therefore, the DWT of the entire HID were in the two ranges, namely, 0.5 1.0 m and 1.0-1.5 m in November. The range of 0.5 1.0 m distributed in Yigan, Yongji and Wulate irrigation region, and the range of 1.0 1.5 m distributed in Jiefangzha and Yichang irrigation region (Fig. 9, 12, 15).

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3.3 Analysis of Temporal and Spatial Distribution of the Groundwater Salinity

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The temporal and spatial distribution of the groundwater salinity was quite similar among the three years (Fig. 16 24). There were two salinity degree zones in the


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Fig. 16. Distributing map of groundwater salinity in March, 2001

Fig. 17. Distributing map of groundwater salinity in July, 2001

northern and southern of HID, which the salinity degree was more than 5000 mg/l (red regions) and even, in some local areas, the salinity degree was more than 10000 mg/l. The northern zone was from Dashuwan (west) to Fenzidi (east), the southern zone was from Xishanzui extended to Chengnan and Shulinzi. The groundwater salinity of the major area of Hetao in March and November was M4000 mg/l, respectively. The reasons caused these results were followed: (1) Because the southwest elevation (1043 m) was higher than that of the northwest (1034 m) and the southeast (1018 m). The groundwater horizontal movement was from southwest toward the northwest and southeast. An amount of salt accumulated into the soil and penetrated into groundwater of the northwest and southeast area of HID. Therefore, the groundwater salinity in southeast and northwest were higher (M>5000 mg/l) than that of (M4000 mg/l. With the lateral seepage of soil water and the horizontal movement of groundwater, a lot of salt of which dissolved into soil-water and shallower groundwater was moved away HID during the period from November to the next March. Then, the groundwater salinity of the major area of HID was M5000mg/l and even, in some local areas, M>10000mg/l. Meanwhile, the special irrigation seasons and climate made the maximum and minimum of groundwater of HID appeared in the specific period of September and March, respectively. The groundwater salinity of the major area of HID was more than 4000mg/l in November. With the lateral seepage of soil water and the horizontal movement of groundwater, some of the accumulated salt was drained away HID. The groundwater salinity of the major area of HID was less than 3000 mg/l except the northwest and southeast area in March. There were many factors which influence the variation of the table depth and salinity of groundwater such as climate condition, soil types, quality and time of irrigation water and the terrain of HID etc, and the temporal and spatial variation of these factors was high in the different area. Therefore, the linear relationship between DWT and groundwater salinity were not significant. In some special areas, however, the distribution of DWT could reflect the distribution of the groundwater salinity. Namely the shallower DWT was, the higher the groundwater salinity degree would be. The deeper DWT was, the lower the groundwater salinity would be. In this study, we get the characteristics of the temporal and spatial distribution of DWT and shallower groundwater salinity. We can make the rational agriculture


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planting structure according with these characteristics. For example, planting the crop which are the salt-resistant and drought tolerant in the high salinity areas in order to reduced the amount of the salt which were brought by the irrigation water, and to increased the amount of irrigation water in the second-autumn period to leach more soil salt. This is quite important to maintain the eco-environmental balance in HID.

Acknowledgements This study was supported by the Changjiang River scholar and creative team development plan (IRT0657), the Ministry of water resources community projects: Ecological irrigation district studies on theory basis and supporting technical system (20071025),the research cooperation projects of China agricultural university and Inner Mongolia agricultural university and China agricultural university graduate innovative research(kycx09113).

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An Efficient and Fast Algorithm for Mining Frequent Patterns on Multiple Biosequences Wei Liu1,2 and Ling Chen1,3 1

2

School of Information Technology, Nanjing Xiaozhuang University, Nanjing, China Institute of Information Science and Technology, Yangzhou University, Yangzhou, China 3 National Key Lab of Novel Software Tech, Nanjing University, Nanjing, China [email protected], [email protected]

Abstract. Mining frequent patterns on biosequences is one of the important research fields in biological data mining. Traditional frequent pattern mining algorithms may generate large amount of short candidate patterns in the process of mining which cost more computational time and reduce the efficiency. In order to overcome such shortcoming of the traditional algorithms, we present an algorithm named MSPM for fast mining frequent patterns on biosequences. Based on the concept of primary patterns, the algorithm focuses on longer patterns for mining in order to avoid producing lots of short patterns. Meanwhile by using prefix tree of primary frequent patterns, the algorithm can extend the primary patterns and avoid plenty of irrelevant patterns. Experimental results show that MSPM can achieve mining results efficiently and improves the performance. Keywords: Biological sequence; Frequent Pattern Mining; Primary Patterns.

1 Introduction Biosequence patterns usually correspond to some important functional (or structural) elements[1] such as conserved sequence patterns, repeated patterns or combinative patterns etc. Hence it is very significative to find such patterns in protein family analysis, transcriptional regulation analysis, and genome annotation etc. The task of biosequence pattern mining [2] is also the key technique for gene recognition, biosequence functional prediction and interactions explanation between sequences. It is one of the most important research areas in biosequence data mining. In the area of data mining, lots of sequential pattern mining algorithms have been proposed in recent years. At present the sequential pattern mining algorithms are mainly classified as two categories: one is for frequent patterns mining on single sequence; the other is for mining in multiple sequences. The former can mine frequent patterns only for single sequence[3-4], and is unable to synchronously analyze the relation between frequent patterns from a certain sequence and those contained in the other sequences. Such analysis is common and necessary in biosequence data mining. For the latter, according to the definition[5] by Agrawal and Srikan in 1995 based on the analysis of transaction data: given a sequence set and a user-specified support D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 178–194, 2011. © IFIP International Federation for Information Processing 2011

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threshold, the problem of sequential pattern mining is to find all frequent subsequences, that is to say, the counts of the subsequence appeared in the sequence set are not less than the minimal support threshold. In 1996, Strikant et al. proposed GSP (generalized sequential pattern mining)[6] which introduced the concept of time and level-wise constraints based on Apriori algorithm. It mines all frequent patterns by the use of bottom-up and breadth-first search strategy. But when the sequence database is a large-scale one, large amount of candidates could be produced and the database should be frequently scanned. Especially when the sequences contain long patterns, large amount of short candidate patterns may be generated, which could cause the problem would be intractable and of lower efficiency. In order to solve this problem, in 2000 Pei et al. put forward an algorithm named Prefixspan[7] based on pattern growth approach. It adopts divide and conquer method and continuously produces much smaller projected databases so as to mine frequent patterns. Since no candidates are produced in the algorithm, search space is greatly reduced. Its main cost is on the construction of projected databases and its performance is much higher than Aprioribased algorithms. Other recent works on sequential pattern mining algorithms have been surveyed in [8] by Han et al. However, because of the particularity and variety of mining requirements for biological data, the previous developed methods can not be applied directly to the large-scale biological data mining. Therefore extensive efforts have been devoted to developing some special mining algorithms for biological data, such as PTR-based algorithms[9-10] by Apostolico et al., ATR-based algorithms[11-18] by Delgrange et al. and TRFinder algorithm[19] by Beason. Later Kurtz presented REPuter[15] algorithm based on suffix tree which overcame the limitation of the length of input sequences. It was based on sequence alignment technique but could hardly find those frequent repeats among DNA sequences. In 2007, Wang et al made researches on searching for the similar repeated segments[20] and then introduced a new criteria of similarity and the concept of SATR(segment-similarity based approximate tandem repeats). They designed an algorithm SUA_SATR [21] based on SUA with no limitations on pattern length during the searching process. Moreover, with the same similarity, the algorithm is faster than other traditional algorithms for the same DNA sequence, although its efficiency should be improved. In 2007 Xiong et al. proposed BioPM algorithm[22] specially for protein sequence mining. They introduce the concept of multiple supports so as to overcome the disadvantages of traditional algorithms and improve its performance and efficiency. But when the minimal support becomes lower, it can not keep its high efficiency since numerous projected databases are constructed. In addition, the algorithm still produces large numbers of irrelevant short patterns during the mining process. In 2009, Guo et al. addressed MBioPM algorithm[23] which is an improvement of BioPM algorithm. Based on a pattern partitioning scheme, the algorithm successfully avoids constructing large amount of projected databases. But when the lengths of the patterns exceed k, it requires a large buffer for frequent patterns mining which resulted in huge memory space cost. Moreover, it also takes large amount of time to align the existing patterns with those in the buffer. All these large time-space costs will cause the low efficiency of the algorithm.

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To overcome the problems of traditional sequential pattern mining algorithms mentioned above, we present a fast and efficient algorithm named MSPM for multiple biosequence mining. The algorithm mines all frequent patterns rapidly based on the prefix tree of primary frequent patterns which reflects more biological meanings. Our empirical studies on the tested data from pfam protein database show that MSPM algorithm can obtain higher performance and efficiency than the traditional mining algorithms.

2 Definitions and Concepts 2.1 The Primary Patterns

∑ be the alphabet, and S = {S 1, S 2,..., S n } be a string of ∑ . Assuming x is a character in ∑ , if for string S, there exists integers 1 ≤ i 1 < i 2 < ... < i m ≤ n

Definition 1. Let

such that

s

i1

= s i 2 = ...... = s im = x , then we call

s (k ) = " s s ik

x

......s i ( k +1) −1 " the kth

ik +1

primary pattern of S with respect to x. Example 1. Let ∑ = {a, b, c} and S = " bacaabcbab " , then the primary patterns of

S with respect to character a are

s (1) = " ac " , s (2) = " a " , s a

a

a

(3) = " abcb " ,

s (4) = " ab " . a

From the definition, we can easily get the following lemma. Lemma 1. For a string S, let its character set be C ( S ) ⊆ ∑ . For an x ∈ C ( S ) , suppose

there are

∑n

x∈C ( S )

nx

n

x

primary patterns with respect to x in S, then we have

∑ s (i ) ≤ S i =1

x

x

and

= S .

Lemma 2. For a string S, summation of the lengths of the primary patterns with respect to all characters in C ( S ) will satisfy: nx ∑ ∑ s x(i) ≤ C (S ) • S . x∈C ( S ) i =1

nx

Proof. By Lemma 1, we can see that

∑ ∑ s (i) ≤ ∑

x∈C ( S ) i =1

x

S = C (S ) • S

x∈C ( S )

Q.E.D Because

C (S ) ⊆ ∑

nx

and

∑ ∑ s (i) ≤ ∑ • S .

x∈C ( S ) i =1

x

C (S ) ≤ ∑

,

it

is

can

be

deduced

that

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181

Lemma 3. For a string S, the average length of the primary patterns with respect to all characters in S will be not more than C ( S ) . Proof. From lemma 2, we know that the summation of the lengths of all the primary nx patterns of S satisfies ∑ ∑ s x (i ) ≤ C ( S ) • S . Furthermore, by lemma 1 we also x∈C ( S ) i =1

know that the number of total primary patterns of S is equal to S . Therefore the average length of all the primary patterns of S will be not more than C ( S ) .

Q.E.D

From the lemmas mentioned above, we can see that all primary patterns can be intercepted in Ο( S ) time by scanning S. The framework of the algorithm for intercepting the primary patterns is described as follows:

（）

Algorithm Intercept S Input: string S; Output: the primary patterns of S; begin For every x ⊆ C ( S ) do k=1; ( k sx ) = ∅ ; Let the first position of x appeared in S be l; s x(k ) = s x(k )U{s l} i=l; repeat While ( s i ≠ x U

s ≠ ∅) s (k ) = s (k )U{s } ; x

x

i

do

i

i=i+1; End while k=k+1 ; s x(k ) = {s i} ; i=i+1;

Until End for End

s

i

=∅

2.2 The Table of Primary Patterns

After getting all primary patterns of S, we can further build a table of primary patterns for S. All the primary patterns are listed in the table in the lexicographic order so as to conveniently search.

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Example 2. For the sequence S = " bacaabcbab " in example 1, after sorting all primary patterns of S, the table of primary patterns can be built as shown in Table 1. Table 1. The table of primary patterns for s

Num 1 2 3 4 5 6 7 8 9 10

s

loc 4 9 5 2 10 8 1 6 3 7

m

a ab abcb ac b ba bacaa bc caab cbab

In the table, each entry is a vector ( Num,

s

m

, loc ) , where Num is the index of the

entry in the table, s m is the primary pattern and loc denotes the start position of s m in S. All the primary patterns obtained by algorithm intercept(S) should be sorted so as to be arranged in lexicographic order. By Lemma 1, we know that there are S patterns. Suppose that S = n , it costs Ο ( n ⋅ log n ) time for sorting. Fortunately, for biosequences, ∑ is a constant integer. For instance, for gene sequences, ∑ = 4 whereas for protein sequences, ∑ = 20 . Hence we can use radix sorting method. Obviously by lemma 3 we know that the average length of primary patterns is not more than ∑ and their length is imbalance. Because of each pattern with different lengths, the traditional radix sorting algorithm can’t be applied straightforwardly. Therefore, we present the following sorting algorithm. Algorithm2 Sort( s m ,

s

m

'' )

Input: s m : the primary patterns of S; Output: s m '' : the ordered primary patterns table; Begin Let the initial character of s mi be x i and l = ∑ . If there is

x <x 1

2

< ... 1, it

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215

shows that X has far more important effect on explaining the variable Y. Learning from Table 2, we can know that health status(X2), education level (X1), vocational skills(X4), training experience(X5) are all significant factors influencing the off-farm employment of the rural area. However, the working area(X3) is inferior in influencing the off-farm employment of the rural area. Recently, government has paid more attention to the labor force of the rural area. With the development of the “Rural Labor Force Training Sunshine Project”, the situations such as low level of education of the migrate workers, lacking necessary vocational skills have been ameliorated to some extent, making them more competitive in the process of the urbanization. Table 2. Values of VIP

Var ID (Primary) X2 X5 X1 X4 X3

M1.VIP[1] 1.16181 1.14651 1.03079 1.00695 0.509152

3.4 The Discovery of the Specific Points Generally speaking, since sample points which contribute excessively to the principle components can produce deviation when analyzing, we are trying to avoid the existence of such sample points. Therefore, we can measure the cumulative contribution rate that sample point i have to the components t1, t2……tn.

Ti 2 =

1 m t hi2 ∑ n − 1 h=1 sh2

(4)

SIMCA-P software use the Tracy statistics

n 2 ( n − m) 2 Ti ~ F (m, n − m) m(n 2 − 1)

(5)

In the equation, n represents the number of the sample points, m represents the number of components used in the regression equation. When

Ti 2 ≥

n 2 ( n − m) F0.05 (m, n − m) m(n 2 − 1)

(6)

it can be confirmed that on the 95% test level, sample point i makes excessive contribution to the components t1, t2……tn. Point i can be defined as the specific point, which can result in deviation when analyzing. According to the Fig. 5, we can see that all the points are in the circumference of the ellipse. No specific point exists.

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Fig. 5. T2ellipse

3.5 Quality of the Data Reconstruction When using the components t1, t2……tn to establish the PLS regression model, because of omitting some original information, the difference of the fitted value and the actual value is too large, which makes it difficult to reconstruct the fitting equation. Under this scenario, we can measure the reconstruction quality of the sample points. According to this method, the distance of the sample points in the X space is:

si = DModX i = In this equation,

eij2 p−m

×

n n − m −1

(7)

eij2 represents the square of the difference of the fitted value and the

actual value of the sample points. n presents the number of the sample. p represents the number of the independent variables. M represents the number of the components in the regression equation. The average distance of the model in the set of sample points is defined as:

sX =

1 n 2 ∑ si n i =1

(8)

The standard distance is:

( DModX , N ) i =

si sX

(9)

3.6 The Predictive Result of the Model Demonstrated from the Fig. 6, all the values of distance vary from 0 to 2, which mean the reconstruction quality of the sample points is uniform.

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217

Fig. 6. The standard distance of the sample points

4 Suggestions and Solutions According to the study, the labor force training, education, vocational skills, and health status are main factors influencing the off-farm employment while working area only has a slight impact. In order to solve the problems mentioned above, the government should increase the training of the rural labor force and the training should focus on improving the professional skills of rural labor force. In order to improve the quality of the rural labor force, we should as well strengthen the rural education, implement variety of effective education forms and continue promoting the “Rural Labor Force Training Sunshine Project”. Only thus can we truly ameliorate the situation of the off-farm employment in the rural area.

References 1. 2. 3. 4.

Du, Y., Piao, Z.: Labor migration income and poverty. China’s Rural Observation (5), 2–9 (2003) Ren, G., Xue, S.: Training and employment income growth of Chinese agricultural mechanization. Impact Study (06) (2009) Xin, L., Jiang, H.: Rural labor non-farm payrolls factors analysis_ Based on a rural labor force of 1006 Sichuan. Agricultural technology economy (06) 2009 Wang, H.: partial least-square regression method and its application. Defense Industry Press, Beijing (1999)

218 5. 6. 7. 8.

Y. Huang and Y. Xu Chen, X., Huang, J.: The factors affecting the migrant workers principal component analysis (18) (2009) Ren, R., Wang, H.: Multivariate statistical data analysis. - theory, method and examples, p. 149. Defense Industry Press, Beijing (2009) Jiang, Y.: Fujian industry structure and employment structure of correlation analysis. Technology (9) (2009) Wang, H., Wu, B., Meng, J.: Partial Least-squares regression of linear and nonlinear method. Defense Industry Press, Beijing (2006)

Analysis of Income Difference among Rural Residents in China Yan Xue, Yeping Zhu, and Shijuan Li Laboratory of Digital Agricultural Early-warning Technology of Ministry of Agriculture of China, Institute of Agricultural Information, CAAS, 100081 Beijing, China {Xueyan,Zhuyp}mail.caas.net.cn

Abstract. This paper studies and analyzes the income difference among Chinese rural residents from 1997 to 2008 through absolute difference indices and relative difference indices. It comes to the conclusion that the absolute income difference among rural residents in China has been increasing year by year, while the relative difference remains around the average level and tends to increase in fluctuations in recent years. The paper also discusses the results and proposes corresponding countermeasures. Keywords: Income Difference, Rural Residents, Analysis.

1 Introduction Since the reform and opening up, China’s economy has maintained a momentum of rapid development. But the income of residents, especially those in rural areas, has not increased along with the economy, causing a big gap between productivity and consumption level, which inevitably hampers the sustainable economic development. Therefore, to keep residents’ income growth in line with economic development has both social and economic significance. As China is a large agricultural country, rural residents’ income growth is especially important, which is also the starting point of this study.

2 Measurement of Income Difference The selection of method and index system for measuring residents’ income difference is directly related to the accuracy and rationality of the results. Currently, there are two kinds of basic indices for studying income difference: one is absolute difference measurement, e.g. standard deviation, weighted standard deviation and average deviation; the other is relative difference measurement, e.g. extreme value deviation, variation coefficient, Gini coefficient, Lorenz curve and Theil index. Generally these indices can reflect the overall difference and changes of residents’ income. The paper analyzes the income difference among Chinese rural residents in recent years through standard deviation, weighted standard deviation, average deviation, Gini coefficient and Theil index respectively. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 219–226, 2011. © IFIP International Federation for Information Processing 2011

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2.1 Standard Deviation (S) Standard deviation reflects the deviation between the regional index and the corresponding arithmetic mean. The greater standard deviation, the greater absolute difference in residents’ per capita income across regions. The formula is: n 2 ∑ ( y j−y ) j N

S =

(1)

Where S is standard deviation; yj is per capita income of rural residents in j region; y is average per capita income of rural residents in different regions; and n is the number of regions. Standard deviation is an intuitive, simple and clear index that is relatively easy to calculate. But as an arithmetic mean based on regional index deviation, it can not fully reflect the scale difference across different regions. 2.2 Weighted Standard Deviation (Sw) Weighted standard deviation is also an easy and effective measurement tool for analyzing regional income difference.

Sw =

n

pj

∑(y − y) * p 2

j

(2)

j

Where yj is the income of rural residents in j region; y is the average per capita income of rural resident in all regions; Pj is the population of j region; P is the population of all regions; and n is the number of regions. The greater Sw value, the greater absolute difference. Compared with standard deviation, weighted standard deviation is obviously much more resistant to the disturbance of region-division method and more stable when it comes to multi-angle analysis of the regional difference. 2.3 Deviation (D) Average deviation is based on the relationship between income distribution and equivalence distribution. It is equal to the expected deviation between the overall income level and the average income level. The greater average deviation, the greater difference between income distribution and equivalence distribution, and vise versa. The formula is: n ∑ yj−y j D = n

(3)

Where D is average deviation; yj is per capita income of rural residents in j region; y is average per capita income of rural residents in different regions; and n is the number of regions.

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2.4 Gini Coefficient (G) Gini coefficient is commonly used to measure inequality of income, consumption or any other things. It calculates the inequality index based on the Lorenz Curve. A practical way is to use a formula deducted by triangle method, i.e. rank income in increasing order and divide the total population into n income groups (no need to divide into equal parts by proportion). Assume the population of the ith group account for Pi of total population, and the income account for Ii (i=1, 2, …, n) of total income, then the cumulative proportion of population from 1st to ith group Mi=P1+… +Pi, and the cumulative proportion of income Qi=I1+…+Ii. The formula of Gini Coefficient is: n−1

G =

∑ ( MiQi − Mi+1Qi )i i =1

(4)

If the Gini coefficient is smaller than 0.2, the society is quite equal in income distribution. Values between 0.2 and 0.3 indicate a high equality; between 0.3 and 0.4, moderate inequality; between 0.4 and 0.5, a high inequality; and greater than 0.6, an absolute inequality. 2.5 Theil Index (T) An index to measure income difference between individuals or regions. The smaller value, the less disequilibrium. A major advantage to measure inequality by the Theil index is to measure the contributions of intra-group and inter-group difference to the total difference. But note that the calculation is complicated, and the income difference represented by Theil index is largely impacted by the sample size. These shall be taken into consideration in calculating income difference with the Theil index. The total level of income difference of regions represented by the Theil index is the weighted total of the logarithm of income share against population share of each region, the weight being the income share. The formula is:

n Y T = ∑ Yi log i Pi i =1

(5)

Where T is the Theil index, n is the number of regions, Yi is the income share of the ith region, and Pi is the population share of the ith region.

3 Empirical Analysis The online version of the volume will be available in LNCS Online. Members of institutes subscribing to the Lecture Notes in Computer Science series have access to all the pdfs of all the online publications. Non-subscribers can only read as far as the

222


abstracts. If they try to go beyond this point, they are automatically asked, whether they would like to order the pdf, and are given instructions as to how to do so. 3.1 Data Source The data in this paper are sourced from China Statistical Yearbook 1997-2008, and basic data including population and rural residents’ per capita net income are selected according to the measurement indices. 3.2 Analysis of Absolute Difference According to results in Table 1, we can observe changes in absolute difference of Chinese rural residents’ per capita net income, as shown in Figure 1. Figure 1 shows intuitively that the absolute difference increases year by year during 1997 and 2008, and the change trend of the three indices are exactly the same. Standard deviation for instance, increases slowly from 910.82 of 1997 to 1252.36 of 2003, and rapidly from 1341.89 of 2004 to 2150.52 of 2008, showing distinct phase characteristics. Table 1. Standard deviation, weighted standard deviation and average deviation of Chinese rural residents’per capita net income from 1997 to 2008 Years

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Example Standard Deviation Weighted (S) Standard Deviation (Sw) 910.82 752.44 928.97 762.43 958.36 792.01 1024.03 859.91 1096.05 903.21 1179.81 967.12 1252.36 1045.74 1341.89 1124.25 1575.31 1304.54 1758.56 1451.91 1940.00 1593.22 2150.52 1757.13

Average Deviation (D) 677.49 700.71 728.78 779.98 837.18 897.31 952.59 1018.11 1178.57 1311.22 1437.33 1586.86

Naturally, from a statistical view, the calculation of standard deviation and other indices are impacted by changes of mean value, i.e. the increase of Chinese rural residents’ per capita net income can be summarized in part as the improvement of overall income per capita. Figure 2 shows that the increase of standard deviation, weighted standard deviation and average deviation of Chinese rural residents’ annual per capita net income is consistent with the rising trend of overall net income per capita. It confirms that the rise of net income per capita contributes to the increase of


223

absolute indices of rural residents’ annual net income per capita. Accordingly, there must be certain error if standard deviation and other indices are used to study the income difference. Therefore, to eliminate the influence of this factor, relative difference indices shall be combined with absolute differences for further analysis.

2500 2000

Standard Deviation (S) Weighted Standard Deviation (Sw) Average Deviation (D)

1500 1000 500 0

… … … … … … … … … … … … 1 1 1 2 2 2 2 2 2 2 2 2

Fig. 1. Changes in absolute difference of Chinese rural residents’ per capita net income from 1997 to 2008

5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

Standard Deviation (S) Weighted Standard Deviation (Sw) Average Deviation (D) average per capita income 7 9 9 1

8 9 9 1

9 9 9 1

0 0 0 2

1 0 0 2

2 0 0 2

3 0 0 2

4 0 0 2

5 0 0 2

6 0 0 2

7 0 0 2

8 0 0 2

Fig. 2. Comparison of standard deviation, weighted standard deviation, average deviation with per capita net income of rural residents

3.3 Analysis of Relative Difference From Table 1, Figure 3 and 4, we can see that from 1997 to 2004, the relative difference of Chinese rural residents’ per capita net income evolved as follows: First, Gini coefficient changed insignificantly from 0.401 in 1997 to 0.4097 in 2008, while obvious fluctuation appeared from 1998 to 2002 as decrease after

224


increase. During the 12 years from 1997 to 2008, Gini coefficient was always around 0.41, which shows that the income gap is relatively reasonable or a little large, and the gap tended to widen especially from 2006 to 2008. Second, the Theil index slightly slipped from 0.3477 in 1997 to 0.3339 in 2008. It is not difficult to find out that the Theil index also fluctuates during the 12 years. In general, however, the disequilibrium of rural residents’ per capita net income appears to be at average level or slightly decreasing. Table 2. Gini coefficient and Theil index of Chinese rural residents’ per capita net income from 1997 to 2008 Years

Gini coefficient (G)

Theil index (T)

1997

0.4010

0.3477

1998

0.4026

0.3511

1999

0.4061

0.3513

2000

0.4109

0.3441

2001

0.4072

0.3564

2002

0.3995

0.3629

2003

0.3924

0.3668

2004

0.3966

0.3538

2005

0.3954

0.3564

2006

0.4005

0.3582

2007 2008

0.4055 0.4097

0.3449 0.3339

Gini coefficient (G) 0.415 0.41 0.405 0.4 0.395 0.39 0.385 0.38 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Fig. 3. Changes in Gini coefficient of Chinese rural residents’ per capita net income from 1997 to 2008


225

Theil index (T) 0.37 0.36 0.35 0.34 0.33 0.32 0.31 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Fig. 4. Changes in Theil index of Chinese rural residents’ per capita net income from 1997 to 2008

4 Discussion and Proposal According to the above analysis, the absolute difference indices of Chinese rural residents’ income over the 12 years from 1997 to 2008 are enlarging year by year, with obvious phase characteristics; the relative difference indices appear to be at average level in general, but the Gini coefficient tends to increase in fluctuations in recent years. Therefore, the rural residents’ income gap is widening in China. Appropriate income gap can stimulate competition and break the absolute equalitarianism to promote economic development. However, as the gap is significantly widening, if we don’t take corresponding measures, the trend will continue for a long term to cause excessive disparity in income, which will easily influence the social stability and hamper the economic development. Therefore, we should fully understand how rural residents’ income gap may influence social economy to correctly handle the relations between efficiency and impartiality. Instead of placing our hope on the natural evolution of economy, we shall consciously and actively adjust and improve development policies for different regions to control the current rural residents’ income gap in a certain range, and allow the existing gap to promote instead of hampering the economic development. To this end, we put forward the following proposals: First, establish corresponding policies and measures to support agricultural development, increase funds to support agriculture, invest more in basic education, support rural infrastructure construction and social undertaking development, identify scientific rural industrial development strategy, and promote industrialization and urbanization in rural areas. Second, increase rural residents’ income, while narrowing their education gap and improving their quality. For low-income areas in particular, we shall encourage intellectual work and investment through macro control while actively improving farmers’ income level so as to increase the income of professional technicians and managers, prevent loss of talents and resources, and ensure the development potential of low-

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income areas. Meanwhile, we shall offer preferential policies to attract the transfer of labor, technology and fund to the backward areas. Third, regulate distribution principle, crack down illegal earnings and adjust tax system. We shall adjust and improve income distribution system, protect legitimate earnings and adopt differentiated support policies for different areas to reduce farmers’ burden. Fourth, another way to increase rural residents’ income is to ensure equal opportunity to earn income. The government should ensure social members have a basic and equal start when they enter the society, i.e. each individual in the society should have equal fundamental rights including the equal right to existence, employment, education and relocation, etc.

References 1. Tao, Y.H.: Study on Regional Difference of Rural Resident Income and Its Influencing Factor. Doctoral Dissertation (2008) 2. Sun, J., Huang, H.B.: Application of Theil Index in the Analysis of Income Gap in East, Middle and West Areas. Market Modernization 500, 51 (2007) 3. Huang, T.Y., Wang, J.G.: Choice of Measurement Index System for Resident Income Gap. Contemporary Economic Research 9, 42–47 (2000) 4. Shang, Y.H.: Reason and Countermeasure Proposal for the Increase of National Gini Coefficient. Theoretical Exploration 2, 84–86 (2007)

Analysis of Secretary Proteins in the Genome of the Plant Pathogenic Fungus Botrytis Cinerea Zhang Yue, Yang Jing, Liu Lin, Su Yuan, Xu Ling, Zhu Youyong, and Li Chengyun* Key Laboratory of Agro-biodiversity and Pest Management of the Education Ministry of China, Yunnan Agricultural University, Kunming, 650201, China [email protected]

Abstract. The signal peptides prediction algorithm SignalP v3.0, subcellular protein location prediction algorithm TargetP.v1.1, potential GPI-anchor sites prediction algorithm big-PI predictor, trans-membrane domains prediction algorithm TMHMM v2.0 and bioinformatics algorithm MEME were used to analyze 16446 protein sequences of Botrytis cinerea. The results showed that there were 579 deduced secretary proteins. Among these proteins, the minimum and maximum of open read frame were 102 bp and 4848 bps respectively and mean score was 1271 bps. The signal peptides’ length was concentrated to 16~39 amino acids and the average length was 21. 122 of these proteins contain the highly conserved host-targeting-motif RxLx within 100 residues adjacent to the signal peptide cleavage site. According to PEDNAT and COG of GenBank database, this motif’s functions include metabolism modification and cell secretion etc. We blast those putative secretary proteins with RxLx motif in GenBenk and found 47.54% of them have highly conserved homologues in other species, among them 74.1% have putative protein domains. This means these proteins may be more stable or earlier origin. We suppose these proteins are candidate participating in the pathogenesis of Botrytis cinerea but we still need more experimental evidence to confirm their definite functions. Keywords: Botrytis host-targeting-motif.

cinerea;

signal

peptide;

secretary

protein;

Botrytis cinerea belongs to Deuteromycotina and is a widespread phytopathogenic fungus causing disease in a substantial number of economically important crops [1]. It causes Gray-mold rot or Botrytis blight and affects most vegetable and fruit crops, as well as a large number of shrubs, trees, flowers, and weeds. It also has a beneficial role in the production of rare dessert wines. The genome sequence information of Botrytis cinerea was released in 2005 and was helpful for us to understand this ascomycete's complex developmental life cycle, Pathogenesis mechanisms and interactions with its different host plants. *



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Protein is the basic function element of living organism. Many pathogenic microbes could secrete kinds of proteins into the host cell to facilitate its infection process [2]. So analysis of secreted proteins in the pathogen’s genome will be helpful to reveal its pathogenesis mechanisms. The secreted proteins used to be synthesized by ribosome and need a transport process to secrete outside the cells. There are two mechanisms for peptides transportation. The fist is cotranslational transfer. In this way, synthesized partial signal peptide combined to endoplasmic reticulum and the secreted proteins were synthesized meanwhile entered the endoplasmic reticulum, after moderated by endoplasmic reticulum and Golgi complex they were secreted outside. The second is posttranslational translocation. By this mechanism the complete proteins were synthesized and then were transported for modifying with the help of leader peptide [3]. In both mechanisms, the signal peptide has played the fundamental role. The signal peptide was usually composed by 10 to 60 amino acids. It contained a hydrophobic region (H-region) which was constituted by 6 to 15 amino-acid residues in the center and hydrophilic N terminal and C terminal at the both sides [4]. According to Gunter Blobel’s signal peptide hypothesis, secretary protein’s destiny was decided by its signal peptide and this peptide will be cut off when the protein arrive its destination. So we can decide whether a protein is a secretary protein by analysis of its signal peptide of N terminal [5]. Several software had been developed to indentify the signal peptide in the protein. Lee used SignalP(v2.0) analyzed 47 secretary protein and 47 other proteins of Candida albicans, it shows that the putative results of this software is credible [6]. The interactions between pathogens and their hosts is a hot spot for scientific research recently. How the secretary proteins entered the plant cells and play their function is still not clear now. Bacteria’s type III secretary system have been illustrated by many researchers, but the pathway of the eukaryotic pathogen’s secretary proteins is still unclear [7, 8]. There is a report revealed that during the process of Plasmodium Falciparum infected erythrocyte, most of secretary proteins which will be injected into erythrocyte contain an RxLxE/D/Q motif at 60 amino-acid residues downstream the cutting site of the signal peptide [9]. Souvik Bhattacharjee also indicated that RxLx motif also existed in hundreds of pathogenic secretary proteins of Phytophthora infestans which play the same function as RxLxE/D/Q motifs of Plasmodium Falciparum. Plasmodium Falciparum and Phytophthora infestans are far related and infect animal and plant respectively; they should have different pathogenic process and mechanism, so RxLx motif may be a conserved signal recognition motif of eukaryotic pathogen [10]. In this study, we try to make use of the genome data to indicate how many secretary proteins contained by Botrytis cinerea and whether RxLx motif exist in this saprophytic fungi’s secretary proteins and play pathogenic function.

1 Materials and Methods

（

The sequence data of Botrytis cinerea was downloaded from the database of BROAD institute Botrytis cinerea strain B05.10. It totally contained 16446 putative

Analysis of Secretary Proteins in the Genome of the Plant Pathogenic Fungus

229

proteins in the database (http://www.broad.mit.edu/annotation/genome/botrytis_cinerea/ Home.html), by the use of signal putative software SignalP (http://www.cbs.dtu.dk/ services/SignalP/) [11], subcellular organelle located software TargetP (http: //www.cbs. dtu.dk/services/TargetP1.1) [12, 13], anchoring protein analysis software big-PIPredicto (http://mendel.imp.ac.at/sat/gpi/gpi_server) [14] and transmembrane helix analysis software TMHMMServer (http://www.cbs.dtu.dk/services/TMHMM) [15], we selected those proteins with signal peptide (satisfied L=-918.235-123.455*(Mean.S.score)+ 1983.44*(HMM scores) and L>0) [6], secreted outside the cells not to other subcellular organelle, not anchoring protein and didn’t contain transmembrane helix as secretary proteins. Then we use MEME(Multiple Expectation Maximization for Motif Elicitaion) [16] to test whether these putative secretary proteins contain RxLx motif and whether there exist conserved amino-acid residue at the two sides of this motif. Then the graph was produced by software LOGO [17] according to the result of MEME. We also compared the sequence of these putative secretary proteins with RxLx motif to PEDNAT database http://pedant.gsf.de/index.jsp and searched them in the COG database from GeBank http://www.ncbi.nlm.nih.gov/COG/old/xognitor.html to find and categorized the putative functions of these proteins. At last, we blast these sequence in the GeBank (BLASTP 2.2.17 (Jun-24-2007) http://www.ncbi.nlm.nih.gov/ try to find the homologues of these proteins and conjectured their functions.

（（

2

）

（

））

Results and Analysis

As figure 1 showed, we get 868 proteins contained the signal peptide in the Botrytis cinerea’s genome, among them 579 proteins which account for 3.52% of the Botrytis cinerea’s genes were predicted to be secretary proteins by the method have been mentioned.

16446 hypothetical proteins

（

After signalpv3.0: N-terminal signal peptide prediction 868 proteins) After TargetP1.1: Protein location analysis (837 secreted proteins) After TMHMMServer2.0: Transmembrane domains analysis (607 proteins) After Big-PI Predictor: GPI-anchor proteins analysis (579 proteins) Fig. 1. Analysis flow chart of Botrytis cinerea secretary proteins

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Fig. 2. The ORF length distribution of 284 secretary proteins in Botrytis cinerea

Fig. 3. The signal peptide length distribution of 284 secretary proteins in Botrytis cinerea


231

Fig. 4. Logo shows the conservation of the RxLx motif from predicted Botrytis cinerea secretary proteins The motifs are highlighted in board letters in the table. Abbreviations for amino acid residues: A, Ala; C, Cys; D, Asp; E, Glu;F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P,Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; Y, Tyr.

Fig. 5. Functional categorization of putative secretary proteins containing RxLx motif which have function descriptions in COG database

The average length of the predicted secretary proteins of Botrytis cinerea is 1271bp; the longest one and the shortest one are 4848bp and 102bp respectively. From figure 2, we can find that the most of them are 500-2000bp, which account for 72.19% of all the genes. The average length of these secretary proteins’ peptide is 21 amino-acid

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residues, the longest one is 39 and the shortest one is 16. The figure 3 showed the length distribution of these proteins’ peptide. 540 of them are 17-25 amino-acid residues which account for 93.3% of all and proteins with 19 amino-acid residues come up to 109 proteins (18.8%). We analyzed the all these putative secretary proteins by MEME software and found that there are 122 proteins (21% of total) contain RxLx motif within the 100 amino-acid residues downstream of the cutting site of signal peptide. The report had showed that Plasmodium Falciparum’s pathogenic secretary proteins had conserved E, D or Q fowled the RxLx motif while Phytophthora infestans didn’t. But from the figure 4, we can found that there more A, G, L and S appeared at the downstream and upstream of the RxLx motif in Botrytis cinerea. A, G and L are all nonpolar amino acid and S is polar neutral amino acid. The most conserved amino-acid residue followed the RxLx motif is D. This is just the same as Plasmodium Falciparum. Then we categorized the putative functions of these proteins by COG of GenBank (figure 5). There are only 26 (21.3%) proteins found in COG and by categorized into 11 different kinds of functions. Most of them are related to amino-acid metabolism (23.08%). For further predicted the functions of our putative secretary proteins, we compared them in the PEDANT database but only 6 of them have the specific function description (Table 1). The most of them are related to cell metabolism and some of them also appeared in the Phytophthora infestans’s pathogenic secretary proteins. Table 1. The functional description of secretary proteins containing the RxLx motif in Botrytis cinerea Gene name

start

end

Functional description

BC1G_15580 BC1G_05799 BC1G_12776 BC1G_04994 BC1G_06540 BC1G_03579

29028 27623 79340 285638 150762 412126

29981 28429 77335 283980 152263 410768

may act as a sorting receptor in the delivery of vacuolar hydrolases, partial peptidylprolyl isomerase tripeptidyl peptidase hypothetical protein similar to alpha-L-arabinofuranosidase hypothetical protein similar to aspartic proteinase precursor hypothetical protein similar to aspartyl protease

Then we blast all these proteins in the GenBank and found that 58 proteins have high conserved homologues (E-value 1×10-20 and identities 40%) in other species (accounted for 47.54% of all predicted proteins) and most of them have putative conserved protein domains (Table 2). Then we selected 7 the most conserved (have more than 50 homologues) proteins and marked out the location of their RxLx motif and protein domains (Figure 6). We can see that in 6 proteins, the motif appeared within the first 10 amino-acid residues of the C terminals of their protein domains.

〈

〉


233

Table 2. The blast results of putative secretary proteins include RxLx motif

Gene name

number of homologs

No. of the

No. of

species

Pathogenic

containing homolog

species

Conserved protein domain

BC1G_00639

5

2

1

Tannase

BC1G_00978

2

1

1

Pro-kumamolisin

BC1G_01009

9

5

4

N

BC1G_01027

18

10

7

Peptidase_S10

BC1G_01073

4

2

1

Pro-kumamolisin

BC1G_01628

6

6

4

Peroxidase

BC1G_01874

7

6

3

Glycine-rich protein domain

BC1G_02021

19

11

8

GMC oxidoreductase; choline dehydrogenase

BC1G_02163

30

24

14

Cerato-platanin

BC1G_02492

2

2

2

N

BC1G_02944

1

1

1

Pro-kumamolisin

BC1G_03275

14

13

8

N

BC1G_03557

24

22

9

N

BC1G_03560

4

4

2

N

BC1G_03579

28

22

7

Asp, Eukaryotic aspartyl protease

BC1G_04705

7

6

3

Peroxidase

BC1G_04994

26

18

9

Alpha-L-arabinofuranosidase B

BC1G_05488

4

4

2

N

BC1G_05765

7

4

3

Pro-kumamolisin

BC1G_05799

58

39

8

FKBP-type peptidyl-prolyl cis-trans isomerase

BC1G_05885

5

4

3

N

BC1G_06035

100

43

19

Glycosyl hydrolase family 7; Fungal cellulose binding domain

BC1G_06328

6

6

4

Bacterial alpha-L-rhamnosidase

BC1G_06540

14

13

11

Asp, Eukaryotic aspartyl protease

BC1G_07149

24

18

11

Peptidase_S10

BC1G_07160

8

8

6

Phospholipase C

BC1G_07483

3

3

2

Esterase_lipase

BC1G_07899

2

1

0

Cutinase

BC1G_08048

5

4

3

Amidase

BC1G_08735

27

21

12

Cerato-platanin Glycosyl hydrolase family 15; The family 20

BC1G_08755

50

21

7

BC1G_09129

13

13

10

BC1G_09495

2

2

2

Tannase

BC1G_09611

5

5

4

DadA, Glycine/D-amino acid oxidases Rossmann-fold NAD(P)(+)-binding proteins

carbohydrate-binding module (CBM20) DnaJ domain

BC1G_10333

4

4

1

BC1G_10397

10

10

6

N

BC1G_10482

2

2

1

N

BC1G_10768

12

8

5

Intradiol_dioxygense_like domain

BC1G_11019

8

8

5

Salicylate hydroxylase

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Y. Zhang et al. Table 2. (continued) BC1G_11134

9

9

6

Survival protein SurE

BC1G_12138

8

6

3

Alpha-L-arabinofuranosidase C-terminus Arginase family

BC1G_12157

58

29

15

BC1G_12171

11

7

4

N

BC1G_12200

18

14

5

Peptidase family M20/M25/M40; Acetylornithine deacetylase

BC1G_12456

9

7

6

Glyco_hydrolase_16

BC1G_12525

4

3

3

Peroxidase

BC1G_12619

2

2

2

N

BC1G_12776

3

2

2

Pro-kumamolisin

BC1G_12932

25

7

2

Tannase

BC1G_13158

2

2

1

N

BC1G_13581

10

6

6

N

BC1G_13855

7

6

3

BglC, Endoglucanase

BC1G_14244

86

62

13

N

BC1G_14398

15

12

8

DadA, Glycine/D-amino acid oxidases

BC1G_14702

100

36

11

Glycosyl hydrolase family 7

BC1G_15580

9

9

6

N

BC1G_15641

5

3

1

tol-pal system beta propeller repeat protein TolB

BC1G_16238

8

6

3

BglC, Endoglucanase

Fig. 6. The location of RxLx motif and putative protein domain of 7 proteins which contain over 50 homologs in other species


3

235

Discussion

By application of software of bioinformatics’ analysis, we found 579 putative secretary proteins and 122 (21%) of them contained Host-Targeting-motif RxLx within the 100 amino-acid residues downstream of their cutting sites in the genome of Botrytis cinerea. The length of most of these proteins and their signal peptide are moderate. RxLx motif spread in the secretary proteins of this kind of saprophytic fungi indicate they maybe play functions in this fungus’s pathogenic secretary pathways. But we still need experiment evidence to conform this hypothesis. By compared these proteins contain RxLx motif in the PEDANT database and COG of GenBank, we seldom found the definite description to them. For those proteins with description in COG, most of them are related to cell metabolism and cellular process. Interestingly, among these proteins, BC1G_06540 is described as an aspartic proteinase precursor. It is had already been revealed that Botrytis cinerea could secreted aspartic proteinase into its host cell during its pathogenic process [18]. Researchers also found that the a pathogens infected the plant, it must break through the plant cell’s physical outline such as cell wall as well as changed the inner environment of host cells to fit themselves. In this process, their secretions must play the most important roles [19]. Considered other 5 RxLx motif containing proteins with definite descriptions in PEDANT database are all enzymes involved in the cell metabolism and the categorization with most proteins by COG is related to amino-acid metabolism, we supposed that maybe Botrytis cinerea need secreted proteinase into the plant cells in its pathogenic process. Blast these RxLx motif containing proteins in the GenBank, we find that 47.54% of them have high conserved homologues in other species and most have the conserved protein domains. This indicates these proteins with RxLx motif are conserved during the evolution process or early originated in the history. Most of the homologues contained in fungi but still some appeared in higher eukaryotes. Among them, BC1G_14702, BC1G_14244, BC1G_05799 and BC1G_06035 contained huge number of homologues distributed in so many different kinds of plants and animals. This indicated that maybe these genes played the irreplaceable role in organisms as well as Botrytis cinerea. BC1G_05799 had been depicted as peptidylprolyl isomerase in both COG and PEDANT database. This kind of protein interconverts the cis and trans isomers of peptide bonds with the amino acid proline and involved in many cellular process. The 6 of 7 RxLx motifs in the highest conserved putative secretary proteins are all located at the N terminal of their protein domains. Interestingly, the description of protein domains contained by these 7 proteins indicated they all maybe involved in the secretion process even in the pathogenic process. Cerato-platanin had been reported as a kind of phytotoxic protein which was secreted by Ceratocystis fimbriata f.sp. platani [20]. FKBP_C is related to protein synthesis and locate of plant plasmids [21]. Glyco_hydro_7 is a kind of glycosyl hydrolase which was secreted to destroy cellulose in the plant cell [22]. SpeB have been reported as a kind of virulent effectors secreted by Group A Streptococcal [23]. GPI8 is related to the synthesis of phosphatidylinositol anchor protein which could anchor it to the cell surface [24]. So these proteins may play

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their functions in the Botrytis cinerea’s secretion pathway and contributed to its pathogenic process. Most of pathogenic proteins reported now are secretary proteins. In this article we predicted 579 secretary proteins and 122 of them contain Host-Targeting-motif RxLx which could be treated as candidate pathogenic proteins of Botrytis cinerea. Although we still need experiment to prove whether these proteins contributed to pathogeneses, find these candidate proteins will accelerate our understandings of pathogenic mechanism of Botrytis cinerea. Many software used to analyze the protein had been proved effective and it is conveniently for us to understand the information lied in the genome by the help of them.

References [1] Hausbeck, M.K., Moorman, G.W.: Managing Botrytis in greenhouse-grown flower crops. Plant Dis. 80, 1212–1219 (1996) [2] Ellis, J., Catanzariti, A.M., Dodds, P.: The problem of how fungal and oomycete avirulence proteins enter plant cells. Trends Plant Sci. 11, 61–63 (2006) [3] Meyer, D.I.: The signal hypothesis — a working model 7, 320–321 (1982) [4] Martoglio, B., Dobberstein, B.: Signal sequences: more than just greasy peptides. Trends in Cell Biology 8, 410–415 (1998) [5] Cheng-Gang, Z., Fu-Chu, H.E.: Bioinfornation methods and practice, pp. 67–69. Science Press, Beijing (2002) [6] Lee, S.A., Wormsley, S., Kamoun, S.: Ananalysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms. Yeast 20, 595–610 (2003) [7] Christie, P.J., Atmakuri, K.: Biogenesis, architecture, and function of bacterial type IV secretion systems. Annu. Rev. Microbiol. 59, 451–485 (2005) [8] Journet, L., Hughes, K.T.: Type III secretion: A secretory pathway serving both motility and virulence (review). Mol. Membr. Biol. 22, 41–50 (2005) [9] Hiller, N.L., Bhattacharjee, S.: A host-targeting signal in virulence proteins reveals a secretome in malarial infection. Science 306, 1934–1937 (2004) [10] Bhattacharjee, S., Hiller, N.L.: The malarial host-targeting signal is conserved in the Irish potato famine pathogen. PLoS Pathogens 2, e50 (2006) [11] Jannick, D.B., Henrik, N.: Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795 (2004) [12] Emanuelsson, O., Nielsen, H.: Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 300, 1005–1016 (2000) [13] Nielsen, H., Engelbrecht, J.: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Design and Selection 10, 1–6 (1997) [14] Gpi, G.P.I.: Automated annotation of GPI anchor sites: case study C elegans. TIBS 25, 340–341 (2000) [15] Krogh, A., Larsson, B.: Predicting transmembrane protein topology with a hiddenMarkov model: application to complete genomes. Journal of Molecular Biology 305, 567–580 (2001) [16] Bailey, T.L., Elkan, C.: Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In: Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, vol. 2, pp. 28–36 (1994)


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[17] Crooks, G.E., Hon, G., Chandonia, J.M., Brenner, S.E.: Web Logo: A sequence logo generator Genome Research. 14, 1188–1190 (2004) [18] ten Have, A., Dekkers, E.: An aspartic proteinase gene family in the filamentous fungus Botrytis cinerea contains members with novel features. Microbiology 150, 2475–2489 (2004) [19] Haldar, K., Kamoun, S.: Common infection strategies of pathogenic eukaryotes. Nat. Rev. Microbiol. 4, 922–931 (2006) [20] Pazzagli, L., Cappugi, G.: Purification, Characterization, and Amino acid sequence of cerato-platanin, a new phytotoxic protein. from Ceratocystis fimbriata f.sp. platani. The Journal of Biological Chemistry 274, 24959–24964 (1999) [21] Li, S., Nosenko, T.: Phylogenomic Analysis Identifies Red Algal Genes of Endosymbiotic Origin in the Chromalveolates. Mol. Biol. Evol. 23, 663–674 (2005) [22] Sulzenbacher, G., Driguez, H.: Structure of the Fusarium oxysporum endoglucanase I with a nonhydrolyzable substrate analogue: substrate distortion gives rise to the preferred axial orientation for the leaving group. Biochemistry 35, 15280–15287 (1996) [23] Kansal, R.G., McGeer, A.: Inverse Relation between Disease Severity and Expression of the Streptococcal Cysteine Protease, SpeB, among Clonal M1T1 Isolates Recovered from Invasive Group A Streptococcal Infection Cases. Infect and Immun. 68, 6362–6369 (2000) [24] Vidugiriene, J., Menon, A.K.: The GPI anchor of cell-surface proteins is synthesized on the cytoplasmic face of the endoplasmic reticulum. J. Cell Biol. 127, 333–341 (1994)

Analysis of the Heat Transfer Performance of Vapor-Condenser during Vacuum Cooling Gailian. Li, Tingxiang Jin*, and Chunxia Hu School of Mechanical & Electricity engineering, Zhengzhou University of Light Industry, 5 Dong Feng Road, Zhengzhou 450002, Henan Province, P.R. China Tel.: +86-371-63556785 [email protected]

Abstract. The heat transfer performance of vapor-condenser has been studied under different temperature of cold trap and different thickness of the frost layer in this paper. The relationship between dimensionless number Nu and Kn is obtained. The results show that the capturing efficiency of cold trap increases with the decrease of the surface temperature of vapor-condenser. The frost accumulated on the surface of vapor-condenser can cause the overall heat transfer coefficient decrease, which has a negative effect on heat transfer of vaporcondenser. Kn has an effect on the heat transfer of vapor-condenser during vacuum cooling. Keywords: Heat transfer performance; Vapor-condenser; Cold trap; Vacuum cooling.

1 Introduction Vacuum cooling is a rapid evaporative cooling method. Vacuum cooling has been successfully used to cool vegetables and flowers since the 1950s [1]. In the recent years, for the safety of foods, a rapid cooling treatment after cooking process should be used to minimize the growth of surviving organisms. Compared with the conventional cooling methods, vacuum cooling has many advantages. Therefore, many researches have highlighted the applications of vacuum cooling for the cooked meats [2-4]. In addition, heat and mass transfer characteristics during vacuum cooling have been investigated. Predictive models can provide much valuable information for the cooling process of large cooked meat joints under broad experimental conditions within a short time. Wang and Sun have developed a mathematical model for describing the vacuum cooling process of the large cooked meat joints [5-7]. A vacuum cooler is a machine to maintain the defined vacuum pressure in a sealed chamber, where the boiling of the water in the cooked meats occurs to produce the cooling effect. Theoretically, only the speed of vacuum pump is high enough to produce the defined vacuum pressure in the vacuum chamber. However, at a low pressure, the volume ratio of steam and water is very large. For example, when the pressure is 1073 Pa, the corresponding saturation temperature is 8 , the specific

℃

*



Analysis of the Heat Transfer Performance of Vapor-Condenser during Vacuum Cooling

Pr Prandtl number; Nu Nusselt number;

Nomenclature

Q0 ˉCold load of vapor-condenser, W ˗ Rv ˉGas constant for water vapor, J mol

hvs ˉSublimation heat of ice, J kg T ˉThe Kelvin temperature, K ˗ m Mass flux, kg s 1 ;

v ˉSpecific volume, m 3 kg P ˉPressure, Pa ˗

1

D ˉGas constant, J mol t Time, s ; A Area, m 2 ˗ d Diameter, m ;˗

K

Enthalpyˈ J

kg

1

K

1

Kn

˗

Knudsen number;

˗ Greeks Thickness,

m˗

ˉDensityˈ kg

;

m

3

˗

The ratio of specific heat;

1

1

˗

J m 1 K 1 s 1˗ 5.7 10 8 W m 2 K 4 ;

Thermal conductivity, Stefan’s constant, Emissivity;

ˉMean free path of free molecular,

W m

Heat transfer coefficient

ho , hi

1

239

1

2

K

1

˗

m;

Subscripts

fr ˉFrost layer˗

˗

C p Specific heat at constant pressure, J kg K t Time, s ; Ft Thermal accommodation factor; F Tangential momentum accommodation factor˗ 1

1

˗

v ˉVapor˗ i Inlet; o Outlet; c Coolant;

3

volume is 120.851 m kg . If the entire vapor is evacuated only through the vacuum pump, the speed of vacuum pump should be very large, many vacuum pumps are required in the vacuum cooler, which is obviously unsuitable. In order to remove the large amount of water vapor and keep the cooling cycle within a reasonable length of time, the vapor-condenser is used to economically and practically handle the large volume of water vapor by condensing the vapor back to water and then draining it through the drain valve. The vacuum pump and the vapor-condenser in the vacuum cooling system are used to remove the water vapor evaporated from the cooked meats. Generally, the temperature of the vapor-condenser is about –30 ~-50 during vacuum cooling. The large temperature difference exits between the surface of vapor-condenser and the water vapor in the vacuum chamber. Consequently, the water vapor will become the frost at the surface of vapor-condenser. The frost formation on the cold surface below 0 acts not only as a thermal insulator between the surface and the water vapor, but also significantly reduces the heat transfer performance of vapor-condenser, which can result in the decrease of the capability of capturing water vapor in the vapor-condenser. In order to improve the capturing efficiency of the vapor-condenser, the evaporation temperature of refrigerant in the vacuum cooler must be lowered. However, the lower evaporation temperature will add the cost of vacuum cooler and energy consumption. Therefore, it is very important to investigate the heat transfer performance of vapor-condenser for designing and optimizing the vacuum cooling system. The phase change heat transfer theory under vacuum pressure is hardly studied. Hong and Leena [8] have modeled the frost characteristics under atmosphere pressure. In the current study, the heat transfer performance of vapor-condenser in the vacuum cooler is investigated. Moreover, the factors of affecting the heat transfer performance of vapor-condenser are also analyzed.

℃ ℃

℃

240

G. Li, T. Jin, and C. Hu

2 Experimental Apparatus The laboratory-scale vacuum cooler as shown in Fig. 1 was built by Shanghai Pudong Freezing Dryer Instruments Co. Ltd. (Shanghai, China). Vacuum cooler has four basic components: a vacuum chamber, a vacuum pump, a vapor-condenser and a refrigeration system. The vapor-condenser is an evaporator in the refrigeration system and a condenser capturing water vapor evaporated from the cooked meats during vacuum cooling. The cooling coil of vapor-condenser is set up in a stainless cylindrical steel, which is enclosed with 30 mm thickness polyurethane foam to prevent heat transfer. The stainless cylindrical steel with vapor-condenser is defined as cold trap. The water vapor is evaporated from the cooked meats during vacuum cooling. The vaporcondenser and vacuum pump removes the water vapor and air to reduce the pressure in the vacuum chamber. Because of the large temperature difference between the cold trap and the water vapor, the large amount of water vapor can enter into the cold trap. One part of water vapor is condensed into water by liquefaction, and the other part of water vapor become frost on the surface of vapor-condenser by solidification.

1-bleeding valve; 2-weight sensor; 3-sample; 4-thermal couple; 5-pressure sensor; 6-vacuum chamber; 7-electronic balance; 8-compute; 9-temperature controller; 10-coolant outlet; 11coolant inlet; 12-cold trap; 13-vacuum pump; 14-pressure controller; 15-I-7018P module Fig. 1. Schematic diagram of the vacuum cooler system

℃

A set of T-type copper-constantan thermocouples with an accuracy of ± 0.1 are used to record the temperature of the cold trap. The mass flux of coolant is measured through supersonic flowmeter (UFLO2000P, USA). The vacuum pressure is measured through the pressure transducer (CPCA-130Z) with an accuracy of ± 0.5 Pa, the range of pressure transducer is 10 Pa~10 Kpa. The mass of the sample and water are measured through the electric balance (JA12002, made in China). Thermal conductivity of


241

frost layer is measured by an unsteady state method using a line heat thermal conductivity probe, based on the design of Sweat as described by Scully [13, 14]. The thickness of frost layer is directly determined by a micrometer having a 0.1mm resolution.

3 Theoretical Analysis of Heat Transfer in Cold Trap 3.1 The Formation Mechanism of the Frost on the Surface of Vapor-Condenser Fig. 2 shows the formation process of frost. The sensible heat is transferred from the water vapor in the cold trap to the frost surface by the temperature difference driving Sensible heat transfer Latent heat transfer Frost surface Heat transfer by conduction

Phase change

Frost layer

Water vapor diffusion

Vapor- condenser surface

Fig. 2. The formation process of the frost

Fig. 3. Ice crystal shape (1) Plate-like forms: (a) plate, (b) simple sectored plate, (c) dendritic sectored plate, (d) fern-like stellar dendrite; (2) Column-like forms: (e) needle crystal, (f) hollow column, or sheath-like crystal

242


force between the water vapor and the frost surface. Some of the transferred moisture deposits on the frost layer, causing the frost layer to grow. The remainder diffuses into the frost layer. The heat of sublimation caused by the phase change of the added frost layer is transferred through the frost layer. The latent heat and sensible heat transferred from the water vapor are then transferred through the frost layer by conduction. The water vapor diffusing into the frost layer changes phase within the frost layer. The frost density increases as a result of this process. The frost layer is a porous medium composed of ice crystal and air. The ice crystal has different shapes during the formation of the frost layer. Ice crystal shapes are classified into main forms: plate-like forms and column-like forms. The microscopic structure of ice crystal is shown as in Fig. 3 [9]. During the formation of the frost, the mass flux through water vapor diffusing into the frost layer can be calculated by the Clapeyron-Clausius equation. The expression is as follows [10]:

m& fr =

Q0 ⎡ ⎛ ρ f ⎞ 0.5 ⎤ λ fr RT (vv − vice ) ⎢1 + ⎜⎜ r ⎟⎟ ⎥ ⎢⎣ ⎝ ρ ice ⎠ ⎥⎦ hvs + ⎛ ρf ⎞ Dv [hvs − Pv (vv − vice )]⎜⎜1 − r ⎟⎟ ⎝ ρ ice ⎠ 2 fr

Where

(1)

Q0 is the refrigeration load of vapor-condenser; hvs is the sublimation heat of ice; R is the gas constant; T fr is the surface temperature of the frost layer; vv and vice are respectively specific volume of water vapor and ice;

ρf r

and

ρ ice

are respectively density of frost layer and ice;

Pv is the partial pressure of water vapor; Dv is the diffusivity of water vapor;

λ fr

is the thermal conductivity of the frost layer, the expression is as follows

[11]:

λ fr = 0.02422 + 7.214 × 10 −4 ρ fr + 1.1797 ×10 −6 ρ fr 2 The density of frost can change during the formation of frost.

(2)

ρ f r can be expressed

as:

dρ f r dt

=

r ⎡ d ⎛ dρ f r − ⎢ ⎜⎜ 2r 4 ⎣⎢ dt ⎝ dr

m& f r

⎞⎤ ⎟⎥ ⎟ ⎠⎦⎥

(3)


243

3.2 Heat Transfer in Cold Trap

℃

℃

The temperature of cold trap is about –30 ~ –60 . The water vapor evaporated from the cooked meats will become the frost at the surface of vapor-condenser in the cold trap. The frost layer at the surface of vapor-condenser gets thicker and thicker with the increment of time. The frost layer is a porous structure composed of ice crystal and air pores. Moreover, the porous structure contains a low thermal conductivity, which reduces the thermal conductivity of the frost layer. Finally, the frost layer results in a significant heat transfer resistance from the water vapor to the surface of vapor-condenser in the cold trap. The relationship between the heat flux and the thickness of the frost layer can be expressed:

q = −λ f r Where

λ fr

ΔT

(4)

δ

is the thermal conductivity of the frost layer;

δ

is the thickness of the

frost layer. Heat transfer coefficient, k is an important index to evaluate the heat transfer performance. Heat transfer coefficient of cold trap is measured by the qusai-stable method in this experiment. The total heat transfer coefficient is expressed as follows:

k=

Q0 A ⋅ ΔTm

(5)

Where A is the total area of heat transfer; the logarithmic temperature difference, ΔTm , can be expressed as:

ΔTm =

Where

Ti − To ⎛ T − Te ⎞ ⎟⎟ ln⎜⎜ i ⎝ To − Te ⎠

(6)

Te is the evaporation temperature.

On the other hand, the total heat transfer coefficient is also theoretically determined by:

k= Where

αv

1 1 α v + δ λ fr + 1 α c ⋅ d1 d 2

is the heat transfer coefficient of vapor in cold trap;

(7)

αc

the heat transfer

coefficient of coolant in coil of vapor-condenser; d1 and d 2 are respectively inner and outer diameters. The refrigeration load of vapor-condenser, Q0 , in Eq. (1) and Eq. (5) can be calculated by the enthalpy difference of refrigerant between inlet and outlet.

244


Q0 = m& c (ho − hi )

(8)

& c is the mass flux of refrigerant; ho , hi is respectively the enthalpy of reWhere m frigerant in outlet and inlet. Q0 is also calculated through heat transfer of gas in cold trap. The expression can be given by: T∞ ⎞ ⎛ Tml 4 4 Q0 = α v A(T∞ − T fr ) + εAσ T∞ − T fr + m& f r ⎜ ∫ C pf r dT + ∫ C p v dT + hvs ⎟ ⎟ ⎜T Tml ⎠ ⎝ fr

(

Where

)

(9)

C pf r , C p v is the specific heat of the frost and water vapor; T∞ is the gas

temperature in cold trap;

Tml is the sublimation temperature.

The vacuum pump and vapor-condenser can cause the reduction of pressure in cold trap. When the gases in cold trap are at low pressure, the slip flow occurs. The relative importance of effects due to the rarefaction of a gas in cold trap can be indicated by Knudsen number ( Kn ), a ration of the magnitude of the mean free molecular path ( λ ) in the gas to the characteristic dimension ( L ) in the flow field. When the slip flow occurs in the cold trap, the gas adjacent to the surface no longer reaches the velocity or temperature of the surface. The gas at the surface of the frost has a tangential velocity and it slips along the surface. The temperature of the gas at the surface of the frost is finitely different from the surface temperature of the frost layer, and there is a jump in temperature between the surface of the frost layer and the adjacent gas. The energy and momentum equations in cylindrical coordinates can be written as [12]:

u

λ 1 ∂ ⎛ ∂T ⎞ ∂T = ⎜r ⎟ ∂x ρC p r ∂r ⎝ ∂r ⎠ μ

∂P 1 ∂ ⎛ ∂u ⎞ = ⎜r ⎟ ∂x r ∂r ⎝ ∂r ⎠

(10)

(11)

The slip velocity as a function of the velocity gradient near the wall of cold trap can be expressed as:

u r =r0 = λ ⋅

F − 2 ⎛ ∂u ⎞ ⎜ ⎟ F ⎝ ∂r ⎠ r = r0

(12)

The temperature jump in slip flow at the wall of cold trap can be written as:

Tv − Tw =

Ft − 2 2γ λ ⎛ ∂T ⎞ ⋅ ⋅ ⎜ ⎟ Ft γ + 1 Pr ⎝ ∂r ⎠ r = r0

(13)


λ

245

F is the tangential momentum accomFt is the thermal accommodation factor; γ is the ratio of specific heat; Pr is the Prandtl number; r0 is the radius of cold trap. Where

is mean free path of water vapor;

modation factor;

The Nusselt number can be given from Eq. (10) and (11):

Nu =

48 2 − Ft 2γ λ 1 36 Kn ⎛ 6 Kn ⎞ 11 − +⎜ ⋅ ⋅ ⋅ ⎟ + 24 ⋅ 1 + 6 Kn ⎝ 1 + 6 Kn ⎠ Ft γ + 1 Pr r0 2

(14)

4 Result and Discussion 4.1 The Influence of the Frost Layer on Heat Transfer in Cold Trap Fig. 4 shows the experimental data for the thermal conductivity of the frost. The time ranges, for which the data were taken, are given on Fig. 4. When the experimental temperature of cold trap is –45 , the average thermal conductivity of the frost layer

℃

−1

−1

during vacuum cooling is 0.1072 W ⋅ m ⋅ K . It can be also found from Fig. 4 that the thermal conductivity of the frost layer increases with the increment of time. This is because the density and the thickness of the frost layer depend on the temperature of the frost and the time. The lower temperature, the more water vapor in cold trap becomes the frost on the surface of vapor-condenser and diffuses into the frost layer, which can increase the density and the thickness of the frost layer. The relationship between the thermal conductivity of the frost layer and the density of the frost layer has been shown in Eq. (2).

Fig. 4. Thermal conductivity of the frost layer

246


Fig. 5 shows the heat transfer coefficient in different thickness of the frost layer. When the thickness of the frost layer is 1 mm, the heat transfer coefficient is about 4.4

W ⋅ m −2 ⋅ K −1 . The heat transfer coefficient is 3.3 W ⋅ m −2 ⋅ K −1 at 5 mm thickness of the frost layer. Which means that the heat transfer resistance increases when the frost layer gets thick. The results show that the experimental data match with Eq. (7).

Fig. 5. The influence of thickness of the frost layer on the heat transfer coefficient

4.2 The Influence of Temperature of Cold Trap on the Capturing Efficiency The capturing efficiency of cold trap can be defined as follows:

η= Where

M pw M tw

× 100%

(15)

M pw is the total captured practical amount of the frost by solidify and water

by condensation in cold trap;

M tw is the captured theoretical amount of the frost by

solidify and water by condensation in cold trap. Fig. 6 shows the capturing efficiency of cold trap in different temperature of cold trap. It can be found that the lower temperature of cold trap is, the higher the capturing efficiency of cold trap is. When the temperature of cold trap is about -55 , the capturing efficiency of cold trap is above 90%. However, if the temperature of cold trap decreases to -40 , the capturing efficiency of cold trap is about 20%. Therefore, the temperature of cold trap should be very low so that the cold trap can capture more water vapor. On the other hand, if the temperature of cold trap is too low, some water vapors become the frost on the surface of vapor-condenser and on the wall of cold

℃

℃


247

trap, the thick frost layer has the low thermal conductivity. Which increases the heat transfer resistance between the surface of vapor-condenser and the water vapor. With the increment of thickness and density of the frost layer, the heat transfer resistance between the surface of vapor-condenser and the water vapor increases, the temperature of cold trap become high so that the capturing efficiency of cold trap gets more and more low.

Fig. 6. The influence of temperature of cold trap on the capture efficiency

Fig. 7. The relationship between Nu and Kn

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4.3 The Influence of Vacuum Pressure on Heat Transfer in Cold Trap It is assumed that the gas in cold trap is diatomic. The ratio of specific heat of diatomic ( γ ) is 1.4. The relationship between Nu and Kn can be given in Eq. (14). Fig. 7 gives the variation between Nu and Kn in the different thermal accommodation factor. Kn is correlated with the vacuum pressure. It can be found that Nu decreases when the vacuum pressure in cold trap decreases. This is because convection heat can reduce at low vacuum pressure. Nu at the high thermal accommodation factor (1.0) is higher than that at the low thermal accommodation factor (0.8). The variation of Nu is opposite to the variation of Kn .

5 Conclusion The heat transfer performance of vapor-condenser has been studied in different temperature of cold trap and different thickness of the frost layer in this paper. The lower the temperature of cold trap is, the more water vapor is captured. At the same time, because of the low temperature in cold trap, water vapor become frost at the surface of vapor-condenser and on the wall of cold trap. The frost layer is a porous medium composed of ice crystal and air. The low thermal conductivity of the frost layer has a negative effect on heat transfer of vapor-condenser. When the accumulated frost at the surface of vapor-condenser becomes thick, the capturing efficiency of cold trap will decrease. In addition, the relationship between dimensionless number Nu and Kn is obtained, the variation of Nu is opposite to the variation of Kn .

Acknowledgements Funding for this research was provided by Henan Provincial Department of Education (P. R. China).

References 1. Briley, G.C.: Vacuum cooling of vegetables and flowers. Ashrae Journal 46(4), 52–53 (2004) 2. McDonald, K., Sun, D.-W., Kenny, T.: The effect of injection level on the quality of a rapid vacuum cooled cooked beef product. Journal of Food Engineering 47, 139–147 (2001) 3. Burfoot, D., Self, K.P., Hudson, W.R., Wilkins, T.J., James, S.J.: Effect of cooking and cooling method on the processing times, mass losses and bacterial condition of large meat joints. International Journal of Food Science and Technology 25, 657–667 (1990) 4. Desmond, E.M., Kenny, T.A., Ward, P., Sun, D.-W.: Effect of rapid and conventional cooling methods on the quality of cooked ham joints. Meat Science 56, 271–277 (2000) 5. Wang, L., Sun, D.-W.: Modelling vacuum cooling process of cooked meat—part 1: analysis of vacuum cooling system. International Journal of Refrigeration 25, 854–861 (2002)


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6. Wang, L., Sun, D.-W.: Modelling vacuum cooling process of cooked meat—part 2: mass and heat transfer of cooked meat under vacuum pressure. International Journal of Refrigeration 25, 862–871 (2002) 7. Wang, L., Sun, D.-W.: Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints. Journal of Food Engineering 61, 231–240 (2004) 8. Chen, H., Thomas, L., Besant, R.W.: Modeling frost characteristics on heat exchanger fins: Parts I, Numerical Model. ASHRAE Transactions, 357–367 (2000) 9. Na, B., Webb, R.L.: New model for frost growth rate. International Journal of Heat and Mass Transfer 47, 925–936 (2004) 10. Kondepudi, S.N., O’Neal, D.L.: Performance of finned tube heat exchangers under frosting conditions. International Journal of Refrigeration 16(3), 175–180 (1993) 11. Yonko, J.D., Sepsy, C.F.: An investigation of the thermal conductivity of frost while forming on a flat horizontal plate. ASHRAE Trans. 73(2), 111–117 (1967) 12. Barron, R.F., Wang, X.M., Ameel, T.A., et al.: The graetz problem extended to slip - flow. Int. J. Heat Mass Transfer 40(8), 1817–1823 (1997) 13. Sweat, V.E.: Thermal properties of foods. In: Rao, M.A., Rizvi, S.H. (eds.) Engineering Properties of Foods, pp. 49–87. Marcel Dekker, New York (1986) 14. Scully, M.M.: The influence of compositional changes in beef burgers and mashed potatoes on their temperatures following microwave heating and their thermal and dielectric properties. MSc Thesis, University College Dublin, Ireland (1998)

Analysis on Dynamic Characteristics of Landscape Patterns in Hailer and around Areas Hongbin Zhang1,2,3, Guixia Yang1,2,3, Qing Huang2,3, Gang Li1,2,3, * Baorui Chen1,2,3, and Xiaoping Xin1,2,3, 1

Hulunber Grassland Ecosystem Observation and Research Station, Beijing 100081, China 2 Key Laboratory of Resource Remote Sensing and Digital Agriculture, Ministry of Agriculture, Beijing 100081, China 3 Chinese Academy of Agricultural Science Insititute of Agricultural Resource and Regional Planning, No.12 Zhongguancun South St., Haidian District, Beijing 100081, China Tel.:+86-10-82109622-138 [email protected], [email protected]

Abstract. This paper analyzed the spatial-temporal dynamic changes of landscape patterns in Hailer and around areas. Firstly, landscape patterns types of research area were divided into water, sand, farmland, city and grassland based on remote sensing images of 1986, 1991, 1996 and 2001 and field investigation. Then the grassland was classified into higher coverage grassland, high coverage grassland, medium coverage grassland and low coverage grassland by Normalized Difference Vegetation Index. Finally, the spatial-temporal dynamic changes of above-mentioned eight kinds of landscape patterns were analyzed using landscape ecology principle. The results indicated that human activities intensified significant from 1986 to 2001in research area. The area of grassland landscape decreased quickly, and the fragmentation extent intensified. The dominant landscape in research area changed from higher-high coverage grassland to medium-low coverage grassland. The expansion of sand landscape is obvious in periphery of road, city and farmland. The grassland vegetation degenerated seriously. Fragmentation of city landscape lightened, and city landscape patches tended to decrease and centralized. Economy development pattern of research area is in a stage that is transforming from extensive pattern to intensive urbanization pattern. Keywords: Landscape patterns, Hulunbuir, Spatial-Temporal Dynamic.

1 Introduction Landscape spatial patterns are strongly connected with dynamic procession (Wu Jianguo et al., 2001; Nagendra H et al., 2006). Biologic factors, abiotic factors and human factors drive landscape patterns spatial-temporal evolution together and restrict development direction of ecological process. Analysis on landscape patterns spatialtemporal evolution can open out driving mechanism and development trend of *



Analysis on Dynamic Characteristics of Landscape Patterns in Hailer and Around Areas

251

ecological process (Su Y et al., 2005; Wu R et al., 2002; Huang Qing et al., 2007; He Chunyang et al., 2001; Zhang Yili, et al., 2006). In the study of grassland degeneration, analysis on the landscape patterns spatial-temporal evolution makes for understanding relations between landscape pattern and grassland degeneration process and provides scientific foundations on grassland ecological system management and recovery(Wang Hui et al., 2006; Wamg Mulan et al., 2007; Wamg Mulan et al., 2007). So many experts and scholars devote themselves to studying landscape patterns spatial-temporal evolution in grassland deterioration process. But most of them focus on highly degraded grasslands. Their natural habitats are not good and they are very easy to be disturbed by outside environment (Li Yuechen et al., 2006; Cao Chengyou et al., 2006; Tao Weiguo et al., 2007; Chen quangong et al., 2007; Chen Quangong et al., 1998; Wang Qian et al., 2007; Li Jianpinget al., 2006; Wu Yunna et al., 2000; Liu Xuelu et al., 2000; Zhang Tao et al., 2007;). So almost nobody studies meadow steppe grassland and their ecology recovery functions are very good. Hulunbuir grassland is one of the grasslands which are conserved most completed (Pan Xueqing et al., 1992). It has unique location features and typical ecological system features and advanced pasture animal husbandry production and management methods. At the same time, it is an important husbandry manufacturing base in northern China fescue grassland (Lo Bo et al., 1997; Lu Xinshi et al., 2002). But since the 1980s, under pressures of economy development and accretion of population, area of farmland and towns has been increasing rapidly in Hulunbuir grassland and degeneration tendency is still very serious. Vegetation productivity has fallen significantly. Sand expands promptly. Landscape spatial pattern changes violently. Especially Halaer area and surrounding area are typical representing regions in which human actions influence most strongly (Liu Dongxie et al., 2007; Komatsu Y et al., 2005; Zhao Huiying et al., 2007; Zhang Deping et al., 2007; Ma Yuling et al., 2004; Nie Haogang et al., 2005). So association study on intra-regional landscape spatial pattern has important academic value and useful efforts in Hulunbuir grassland ecological system management and recovery (Ren Jizhou et al., 1998).

2 Material and Method 2.1 Survey Region Overview Hulunbuir grassland is located in the west of Daxinanling and from east to west distributed regularity. It spans forest steppe, meadow steppe and steppe. It is one of potential grass yield and optimal herbaceous regions in Inner Mongolia grassland. We selected central region of Hulunbuir grassland as survey region including Hailer, Old Barag Banner, parts of Evenk Autonomous Banner and the whole area is 3160.82km2. In survey region, hydrothermal condition is very good and it belongs to temperate continental climate. Hailer River and Yimin River mixes here. There are abundant water resource and 110 days frost free period. Mean annual temperature is 2 .The soil is mainly chernozem. The area of farmland is very large. Main land

℃

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types include city, farmland, grassland, sand and water. It has convinent transportation and rich economies. It also is population accumulation area in Hulunbuir grassland. Especially Hailer is political, economical and cultural centre and main resources collection and distribution point of Hulunbuir. It also is the point of human action maximum intensity region in Hulunbuir grassland. 2.2 Remote Sensing Data Analysis The selected Data is three terms 1/4 view LandSat-5 TM data and one term 1/4 view LandSat-7 ETM data from earth station of Chinese Academy of Sciences. Table 1. The parameters of remote sensing data Number

Orbit

1 2 3 4

123/25 123/25 123/25 123/25

Imaging Time 1986.8.6 1991.8.4 1996.7.16 2001.7.22

Satellite LandSat5 LandSat5 LandSat5 LandSat7

Average Cloud 0.38 of study image, the non trees areas are be shielded.

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Fig. 1. Quad tree segmentation

(3) Treetop selection. Treetop selection uses non maximum oppression method to distinguish tree top from image object segments. The local maximum of ratio of near red band (NIR) of pixel (defines in formula (1)) is selected as treetop seed which is as center in a window within surrounding 3 image objects. If the two equal local maximum are found, they both be accepted as treetop seed and be marked. (4) Seed growing. This step is to get tree crown extent. The condition of seed growing is set down as mean ratio of near red band (NIR) (defines in formula (2)) of candidate image object element and seed greater than 0.9 and lesser than 1.[8] (5) False treetop seed wiped off. This is necessary because anterior steps get many seeds which are not true treetop anyway. Computing the mean NDVI value and mean red band standard deviation value (defines in formula (3)) of seeds, the false treetop is cognized by much smaller value in theses two index. That means that the preserving seeds are true treetop.

ratio L = μ L

∑μ i =1

i

(1)

1 n ⋅ ∑ vi n i =1

(2)

nL 1 ⋅ ∑ (υ i − μ L ) 2 n − 1 i =1

(3)

μL = δL =

nL

(6) Tree crown shape optimization. The crown boundary is not smooth enough, so some cycling segmentation is done on image objects elements enveloping the treetop objects. Here the quad tree segmentation performs by recursively combining (merging) the image segments as leafs and regions to get more smooth canopy outline. By comparing the character of smaller image objects elements enveloping the treetop objects with the treetop objects, some smaller image objects elements will be combined into the ambient tree crown object.

3 Research Area and Data The presented approach selects a research area for analyzing Quickbird satellite images in Populus×xiaohei plantation even stand at Xue JiaZhuang wood farm in Shanxi

Automated Extracting Tree Crown from Quickbird Stand Image

307

Province of China which location are presented in Fig. 1, because Populus is a very popular broadleaf and has important value in use. The research area’s east longitude is 112033″,north latitude is 39018″,its average year air temperature is 7 ;its average year precipitation is 400mm; its average year evaporation is larger than 2700mm. It is drought and the forest soil is bare. The dominant specie in research area is Populus×xiaohei which planted in April 1977 has 21.6 hm2 area. The terrain of research area is plain. The soil type of research area is meadow soil. By programming ordering, the Quickbird imagery covering the research area on 6 May 2004 was gained, which has pan band and multi spectrum bands. The quality of image is good and there is no cloud on the image. The geodetic coordinate of up left corner of the image is 635167.20m,4352983.20m and down right is 635664.60m,4352655.00m. The 30 sub compartment of Xue JiaZhuang wood farm corresponding the above Quickbird image with 501*344 pixels was selected for tree crown extraction. In the surveying table of 30 sub compartment, the value of canopy closure is 0.7. In May 2004, we surveyed this area. Considering the growth condition of the stand, we selected 3 kinds of plantation density stands, which is 2m×5m (1000 trees/ha) 4m×5m (500 trees/ha 4m×10m 250 trees/ha). In every plantation density, 3 standard sample plots were set up. In total, 9 standard sample plots

℃

、

）、

（

Fig. 2. Regional map of research area

Fig. 3. Location of research stand on Quickbird image

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G. Deng et al.

were gained. The area of every standard sample is 900m2(30 m×30m). The standard samples of 2m×5m plantation density named A1,A2,A3, the standard samples of 4m×5m plantation density named B1,B2, B3, the standard samples of 4m×10m plantation density named C1,C2,C3. The location, tree height and tree diameter of all these trees were measured and these trees had been marked on the printed image photos. Using these truth data, the auto and semi auto tree crown recognition algorithm can be validated.

4 Implement and Results In the algorithm implemented period, there are some parameters must be selected cautiously. In Primary segmentation, the small value of segmentation scale must be selected. After testing from 3,5 and else values, we select 3 for segmentation scale, 0.3 for weightiness of shape and 0.7 for weightiness of compactness. In seed growing step, the mean ratio of near red band (NIR) of candidate image object element and seed is greater than 0.9 and lesser than 1. In the quad tree segmentation for crown shape optimization, the time of cycle is set 3. Table 1 shows the some key features and values of parameters used in this algorithm. Fig. 4 is the image of plot C1 and the Fig. 5 is the 3d view of its spectral values.

Fig. 4. Image of plot C1

Fig. 5. 3d View of plot C1’s spectral values

The result of above method is a tree crown map from the Quickbird image. Fig. 6 is the stand tree crown image map with whole sub compartment. The validation method is carried in 9 standard samples on stand image by automated tree crown recognition to the manual delineation after field work described in section 3. The two results are overlaid, and each tree crown image object, for each layer, is assigned to the object in corresponding layer for which it has the greatest overlap in area. A correct tree crown occurs when a tree crown image object from the recognition algorithm and a tree crown image object from manually delineation are assigned uniquely to each other.[10] Three types of errors are defined for the comparison. Firstly, dissection occurs when more than one image object from the recognition algorithm is associated with the same manual tree delineation. Secondly, aggregation is when more than one image object from the manual tree delineation associated with a single tree crown image

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Table 1. Features and values of parameters in this algorithm

Function of features Distinguish vegetation Treetop seeds

Seed grows conditions Crown shape Optimization

Features Mean NDVI Ratio of NIR

Value of parameters >0.38 Max

Image object window size

3

Mean ratio of NIR Cycle times

(0.9,1) 15 m [1], the impact of groundwater on agricultural soil moisture was small. The impact of cultivation on the soil environment is mostly in the upper layer, thus soil environmental characteristics only within the depth of 0–100 cm are discussed. 2.1 Measurement of Soil Moisture Volumetric water content of plot soil was measured with a neutron gauge (CNC503DR model by Beijing Nucleon Apparatus Company) during 1 January to 31 December 2009: every 10 cm was taken as one layer within 0–60 cm, and every 20 cm within 60–100 cm, and measurement was conducted once every 5 d. Precipitation was field-measured with a rain-gauge bucket at a meteorological observation site close to the test plot. 2.2 Sampling and Analysis of Soil Nutrient Soil sampling was from the farmland and control plots after cotton harvesting in October 2009. Three points were selected at random in each plot, and samples collected from five soil layers: 0–20, 20–40, 40–60, 60–80 and 80–100 cm. All samples were placed in plastic bags, closed with a seal and sent back to the laboratory for drying in the shade and root-removing treatment. Seven indexes of soil nutrients were analyzed by standard soil test procedures [14], i.e. soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP) and available potassium (AK). Soil samples from each plot were also used for particle-size analysis using a laser granulometer (Mastersizer 2000, Malvern Instruments, England). 2.3 Analysis and Evaluation of Soil Nutrient Indexes To comprehensively reflect the status of soil nutrients, soil nutrient properties were analyzed and compared using the soil retrogression index (RI) [15]. For specific calculation and analysis, the soil properties of the control plot were used as the baseline. RI value of each soil layer of farmland was obtained using the formula that follows: n

RI = ∑ ⎡ ( xi − xi′ ) / xi′ ⎤ × 100% / n ⎣ ⎦ i =1

(1)

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Where, RI is soil-nutrient composite index, xi′ is measured soil nutrient index of each layer from the control plot, and xi is the measured value of the same-layer soil nutrient index from the farmland plot (SOM, TN, TP, TK, AN, AP and AK). If RI > 0 for each layer, this indicated that soil nutrient composite indexes from the layer of the farmland plot was higher than that from the same layer of the control; and vice versa if RI < 0. 2.4 Statistical Analysis Significance of differences between means was compared by t-tests, respectively, between soil nutrient indexes of farmland and control plots. One-way ANOVA was used to compare soil characteristics of the different layers in farmland and control plots, respectively. The least significance range (LSR) method was used for multiple comparisons, and Pearson’s correlations method for correlation analysis. ANOVA, LSR and Pearson correlations were all implemented with SPSS 16.0 software.

3 Results and Analysis 3.1 Differentiation Characteristics of Soil Moisture There was one year of continuous observations in 2009 of mean soil moisture variation within the soil depths of 0–100 cm in farmland and control plots (Table 1). Table 1. Soil moisture discrepancy test at different depth of two plots

Depth (cm)

Between plots (t-test) Sig. (2-tailed) *

Within plot (analysis of variance) FP

CP

0–10

0.026

3.97±1.77 e

2.60±0.73 f

10–20

0.002**

6.10±2.31 de

3.38±1.06 ef

20–30

0.000**

7.67±2.67 de

3.85±0.87 de

30–40

0.000

**

9.06±3.18 cd

4.24±0.81 cd

40–50

0.000**

10.70±4.04 bc

4.63±0.91 bcd

50–60

0.000**

13.16±5.02 ab

4.95±1.14 abc

60–80

0.000**

14.83±4.19 a

5.17±1.02 ab

12.03±3.53 abc

5.50±1.13 a

80–100 F Value

0.000

**

**

13.383

12.193**

Values in each column with the same letter are not significant (LSR) between different soil depth; * P < 0.05; ** P < 0.01.

Characteristics of Soil Environment Variation in Oasis–Desert Ecotone

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There was a significant difference within 0–10 cm between farmland and control plots (P < 0.05), and a significant difference within all the other layers (P < 0.001) (Table 1). The maximum value (14.83%) was at 60–80 cm for the farmland plot and the minimum value (3.97%) was at the surface layer. This differed to the control plot where the maximum was at 80–100 cm and the minimum at the surface layer. Soil moisture of the two plots also showed obvious differentiation characteristics and a particular regularity over time. The soil moisture status at different depths, for each month of the one year is shown in Fig. 1.

Depth:10~20cm 24.0 Soil water content (Vol,%)

Soil water content (Vol,%)

Depth:0~10cm 24.0 20.0 16.0 12.0 8.0 4.0 0.0 Jan

Feb

Mar

Apr May

Jun

Jul

Aug Sep

20.0 16.0 12.0 8.0 4.0 0.0

Oct Nov Dec

Jan

Feb Mar

Apr May

Time(months)

Depth:20~30cm

20.0 16.0 12.0 8.0 4.0 0.0 Jan

Feb Mar

Apr May

Jun

Jul

Aug Sep

12.0 8.0 4.0 0.0 Jan

Feb Mar

Apr May



12.0 8.0 4.0 0.0 Jun

Jul

Aug Sep

12.0 8.0 4.0 0.0 Jan

Feb Mar

Apr May

Depth:60~80cm Soil water content (Vol,%)


12.0 8.0 4.0 0.0 Jul

Time(months)

Jul

Aug Sep

Oct Nov Dec

Depth:80~100cm

16.0

Jun

Jun

Time(months)

20.0

Apr May

Oct Nov Dec

16.0

Oct Nov Dec

24.0

Feb Mar

Aug Sep

20.0

Time(months)

Jan

Jul

Depth:50~60cm

24.0

16.0

Apr May

Jun

Time(months)

Depth:40~50cm

Feb Mar

Oct Nov Dec

16.0

Oct Nov Dec

20.0

Jan

Aug Sep

20.0

Time(months)

24.0

Jul

Depth:30~40cm

24.0 Soil water content (Vol,%)


24.0

Jun

Time(months)

Aug Sep

Oct Nov Dec

24.0 20.0

FP

16.0

CP

12.0 8.0 4.0 0.0 Jan

Feb Mar

Apr May

Jun

Jul

Aug Sep

Time(months)

Fig. 1. Seasonal variation of soil moisture at different depths of two plots

Oct Nov Dec

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The soil moisture of each layer of the farmland plot was generally always higher than the same layer of the control plot within 0–100 cm during the different periods (Fig. 1). However, during the fallow periods of January–March and October– December, the difference between farmland and control plots was small, in particular at a depth of 30 cm. During the farming period of March–September, the farmland plot had obviously higher soil moisture than the control plot. The soil moisture content curve for the farmland plots was multimodal at 0–30 cm, bimodal at 40–80 cm, and unimodal at 80–100 cm, i.e. a rule of time variation from multimodal to unimodal presented itself with increased depth. Variation of soil moisture content at different depths of the two plots over different periods is shown in Fig. 2. The difference in soil moisture between farmland and control plots at different depths was clear (Fig. 2). Soil moisture of the control plot had a generally monotonic increase with increased depth. There was a unimodal increase in the farmland plot with

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increased depth; increasing first and then decreasing, with the peak during July– September at depths of 50–60 cm, and the peak for other months all at depths of 60– 80 cm (average soil moisture was 14.6%), similar to the annual mean at this depth (Table 1). Pearson’s correlation analysis indicated that average soil moisture of each layer of the farmland plot within 0–100 cm had no significant correlation with precipitation; neither did average soil moisture farmland at 0–10 cm with irrigation volume. However, at 10–20 cm soil moisture was significantly (P < 0.05) and positively correlated with irrigation volume, as was soil moisture of each layer within 20–100 cm (P < 0.01). Soil moisture of the control plot at 0–10 and 10–20 cm was significantly (P < 0.01) and positively correlated with precipitation. However, there was no significant correlation with precipitation for soil moisture of each layer within 20–100 cm. 3.2 Comprehensive Variation Characteristics of Soil Nutrient Index Based on t-tests of nutrient indexes of the two plots, SOM, TN, TP, AN and AP contents of farmland were all significantly (P < 0.05) higher than those of the control plot at 0–20 cm. There were no significant differences in the other indexes between farmland and control plots; however, the AK content of farmland was somewhat lower. The difference in SOM, TN and AP contents was significant between farmland and control plots at 20–40 cm, but not in any other index except that AK of the farmland plot was higher than that of the control. At 40–60 cm, AP content of farmland was Table 2. Soil nutrient attribute mean and variance analysis of every layer of two plots (mean ± SD) Plot

FP

CP

Depth (cm)

SOM(g/kg)

TN(g/kg)

TP(g/kg)

TK(g/kg)

AN(mg/kg)

AP(mg/kg)

AK(mg/kg)

0–20

4.15±0.08a

0.23±0.01a

0.61±0.02a

23.87±0.32a

10.60±0.87a

15.61 ±5.02a

118.33±13.58b

20–40

3.27±0.36ab

0.16±0.02b

0.59±0.02a

23.77±0.34a

2.77±2.20c

5.54 ±0.38b

153.67±31.21b 198.00±36.43a

40–60

2.68±0.68ab

0.14±0.06b

0.57±0.06a

24.48±0.98a

6.07±1.07b

1.61 ±0.40b

60–80

2.80±0.39b

0.14±0.04b

0.59±0.05a

24.78±0.61a

4.73±1.20bc

4.71 ±0.32b

129.33 ±4.16b

80–100

2.41±0.77b

0.12±0.03b

0.59±0.04a

23.78±0.67a

3.54±0.88bc

3.15 ±0.34b

137.33 ± 9.64b

F Value

5.284*

4.667*

0.448ns

1.641ns

15.948*

21.782*

6.024*

0–20

2.20±0.24a

0.10±0.01a

0.57 ±0.01a

23.60±0.04a

1.67±1.13 b

2.33±0.96a

154.67±25.72a

20–40

2.11±0.20a

0.11±0.02a

0.58 ±0.02ab

23.78±0.27a

2.43±1.56 b

0.92±0.30b

135.67±13.32ab

40–60

2.03±0.10a

0.10±0.02a

0.54 ±0.01bc

23.58±0.66a

9.39±2.21 a

0.67±0.14 b

115.67 ±7.51b

60–80

2.02±0.22a

0.11±0.01a

0.54 ±0.02bc

24.17±0.52a

4.63±1.02 b

1.11±0.22 b

113.33 ±4.58b

80–100

2.21±0.31a

0.11±0.01a

0.53 ±0.01c

23.08±0.96a

3.14±1.66 b

1.15±0.16 b

121.33 ±7.51b

F Value

0.486ns

0.306ns

5.680*

1.345ns

11.455*

5.514*

4.179*

Values in each column with the same letter are not significant (LSR) between different soil depth within each plot; * Significant at P < 0.05; ns: Not significant at P < 0.05.

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significantly higher than that of the control plot, but not for any other soil nutrient index, although all soil nutrient indexes of farmland were somewhat higher. At 60–80 cm, SOM content of farmland was significantly higher than that of the control plot, but not for any other soil nutrient index. At 80–100 cm, there were no significant differences in all soil nutrient indexes between farmland and control plots. Multiple comparisons among the nutrient indexes at different depths for the two plots are shown in Table 2. ANOVA and LSR multiple comparison results showed that soil nutrient discrepancies at different depths of the farmland plot were reflected mainly in SOM, TN, AN, AP and AK indexes; there were no significant differences in TP and TK indexes. SOM, TN, AN and AP contents of farmland at 0–20 cm were all significantly higher than those of layers deeper than 20 cm. There was no significant difference in SOM content of layers deeper than 20 cm. Within 0–60 cm, SOM content showed a decreasing trend with increased depth. SOM content had a significant impact on TN content [16], and so TN content showed a decreasing trend with increased depth within 0–100 cm depth. AK content of farmland at 40–60 cm was significantly higher than in all other layers; however, there was no significant difference in AK content of other layers. There were no significant differences in SOM, TN and TK contents of each soil layer of the control plot within 0–100 cm. Discrepancy in TP content was mainly at 0–40 and 80–100 cm, with no other significant differences in TP content in other layers. The discrepancy in AN index was reflected in AN content at 40–60 cm, which was significantly higher than in any other layer; there was no significant difference in AN content among other soil layers. AP content at 0–20 cm was significantly higher than that of other layers; however, there was no significant difference among other soil layers. AK content of soil layers within 0–20 cm was significantly higher than that of all other layers deeper than 40 cm, but there was no significant difference in AK content of soil layers deeper than 20–40 cm. Composite index RI of soil nutrients in each layer of the farmland plot is shown in Fig. 3. Within 0–100 cm, RI > 0 for all layers of the farmland plot, indicating that the composite index of soil nutrients in each soil layer was higher than that of the control plot. At 0–20 cm, RI of farmland was 186% higher than in the control plot, indicating that after oasis–desert ecotone soil was reclaimed and became farmland, that the long-term irrigation and fertilization management increased soil nutrient levels. RI was 91% higher at 20–40 cm, indicating that soil nutrient conditions of this layer had greatly improved compared with the control plot. Within 40–100 cm, RI of each layer was < 40% and decreased gradually with increased depth, indicating that farmland reclamation only improved soil nutrient conditions of the oasis–desert ecotone within a certain depth, i.e. improvement was gradually weakened with increased depth. The soil nutrient composite indexes between each layer deeper than 20 cm and the surface layer (0–20 cm) of both plots were compared, i.e. the soil properties of each plot at 0–20 cm were taken as the baseline. RI of layers at 20–40, 40–60, 60–80 and 80–100 cm of farmland and control plots were calculated with formula (1), and all results were negative (Fig. 4), indicating the soil nutrient composite indexes of both farmland plot and control plot at 0–20 cm were higher than those of deeper layers down to 100 cm.


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RI(%) 0

20

40

60

0-20

100

120

140

160

180

200

185.85

20-40

90.53

) m c ( h 40-60 t p e D 60-80

80

37.11

15.90

80-100 11.95

Fig. 3. Composite index of soil nutrient in every layer within 1-m depth of the farmland plot (taking control plot as baseline) RI(%) -35

-30

-25

-20

-15

-10

-5

0 0.00

FP CP

0-20 20-40 40-60

) m c ( h t p e D

60-80 80-100

Fig. 4. Composite index of soil nutrient in every layer deeper than 20 cm of both plots (taking 0–20 cm as baseline)

3.3 Differentiation Characteristics of Soil Particle Size The soil particle-size distributions of each layer of both plots for 0–100 cm were within the range 0.35–2000 μm (Table 3). The t-tests indicated no significant differences in clay contents between farmland and control plots at 0–20 cm, but the value of the farmland plot was slightly higher than that of the control plot. Silt contents of farmland were significantly higher (P < 0.01), and sand contents significantly lower (P < 0.01) than the control plot (Table 3). There

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Table 3. Soil size distribution of both plots (mean ± SD) Plot

FP

CP

Depth/cm 0–20 20–40 40–60 60–80 80–100 0–20 20–40 40–60 60–80 80–100

Clay (0.35–2.0μm) 1.84±0.41 1.95±0.38 1.67±1.11 1.87±0.38 1.97±0.27 1.13±0.90 1.69±0.85 1.85±0.40 1.59±0.91 1.86±0.51

Soil size composing /% Silt (2.0–50μm) Sand (50–2000 μm) 28.05±4.28 70.11±4.65 27.70±5.34 70.35±5.69 25.55±10.31 72.79±11.26 26.54±4.72 71.60±5.02 26.14±5.96 71.89±6.20 20.95±4.33 77.92±5.12 24.95±5.32 73.36±6.12 24.51±5.23 73.65±5.62 23.78±5.18 74.63±5.76 25.23±6.63 72.91±7.14

were no significant differences in clay, silt and sand contents between farmland and control plots at depths of 20–40, 40–60, 60–80 and 80–100 cm, respectively (P < 0.05). The impact of cultivation on variation of soil particle-size showed a particular regularity: silt and clay contents in farmland clearly increased and sand contents decreased. ANOVA indicated no significant differences in clay contents of each layer between farmland and control plots within 0–100 cm (P < 0.05), and similarly for silt contents and sand contents. The soil layer with the highest clay contents in the farmland plot was at 80–100 cm, the highest silt contents at 0–20 cm, and the highest sand contents at 40–60 cm. The soil layer with the highest clay contents in the control plot was at 80–100 cm, the highest silt contents at 80–100 cm, and the highest sand contents at 0–20 cm.

4 Discussion The research on soil moisture of the oasis–desert zone in the middle reaches of the Black River within depths of 0–50 cm indicated that soil moisture content of the surface layer in a oasis–desert ecotone was lower than that of oasis farmland [17]. A similar result was obtained in our study, and in addition the soil moisture content of each layer within 0–100 cm in the natural status of the oasis–desert ecotone was found to be lower than that of farmland. The research on soil moisture characteristics of the oasis–desert ecotone at Minqin indicated a vertical variation of soil moisture with an increasing trend from surface to deeper layer within 0–120 cm [18]. Soil moisture of the desert–oasis ecotone in the middle reaches of the Black River within depths of 0–200 cm increased with increased depth [19]. Our results indicated that within 0–100 cm, the soil moisture of the control plot in the oasis–desert ecotone had a generally monotonic increasing trend with increased depth, consistent with previous findings. Because of poor soil texture in the surface layer of the control plot, weak water-retention capacity and the impact of evaporation and infiltration, the soil moisture content of the surface layer was significantly lower than that of deeper layers.


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The research findings on Kerqin sandy land indicated that precipitation influenced soil moisture content of the surface layer at 0–0.8 m depth only, due to the consumption of most precipitation by vegetation via transpiration [20]. In this paper, Pearson’s correlation analysis indicated a significant positive correlation between soil moisture of aeolian sandy soil of the control plot at 0–20 cm and precipitation, while no such correlation occurred for layers deeper than 20 cm, indicating that precipitation had an impact on soil moisture content mainly at 0–20 cm, and below that depth the soil moisture may be affected by other factors such as infiltration. Usually agricultural soil moisture is affected by various factors such as precipitation, irrigation volume, crop growth and climatic conditions. Some findings have indicated that farmland moisture characteristics of arid areas in northern China are affected mainly by evapotranspiration rate [21]. In the event of no groundwater recharge, then precipitation or irrigation would be the main sources of water for farmland, and different precipitation (irrigation) volumes will induce variations in soil moisture [22]. Soil moisture of the farmland plot showed a unimodal increasing trend with increased depth, which increased first and then decreased; of these the peak value was during July–September at a depth of 50–60 cm, and was the result of inconsistent irrigation volumes and irrigation intervals. The soil moisture of each layer of the farmland plot was significantly and positively correlated with irrigation volume within 10–20 cm (P < 0.05) and within 20–100 cm (P < 0.01), indicating that irrigation was the major factor affecting agricultural soil-moisture characteristics within a depth of 1 m. Research by Sun showed that soil moisture during the cotton growth period at Cele was 6.64–13.3% with irrigation [23]. The average soil moisture range of the farmland plot (0–80 cm) was 10.83–12.89% during the cotton growth period (April–October) in this paper, similar to the findings of other researchers – the results can be taken as a guide for local field irrigation. The acquisition, accumulation and consumption of soil organic matter, nitrogen and phosphorus differ according to diverse land uses and soil tillage [24]; and coverage variation caused by oasis growth also had a significant impact on soil nutrient characteristics [1]. The oasis–desert ecotone has less organic matter accumulation under an original state, while farmland becomes fertile with application of farmyard manure and inorganic fertilizer during growing seasons following crop planting. A composite index of soil nutrient was obtained, based on various soil properties, which effectively reflects soil quality and is helpful for visual comparison and evaluation [25, 26]. The comparison of RI between the two plots showed that farmland had significantly higher values than that of the control plot, indicating that soil nutrient improvement in the ecotone was positive with certain substance and energy inputs. Soil nutrients reached a maximum in both plots at 0–20 cm, possibly indicating that irrigation and cultivation factors had an impact mainly on the cultivated horizon of farmland; while litter fall of natural vegetation in the ecotone participated in the nutrient cycle, also making soil nutrients in the surface layer of the control plot relatively higher. Silt and clay contents of the farmland plot were higher than those of the control, but sand content was lower presumably due to the impact of artificial irrigation and cultivation on accumulation of fine soil particles during the oasis expansion process [11]. Sand and silt contents were the major component in upper and lower layers of both

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farmland and control plots, and the proportions were: sand > silt > clay (< 2.00%), which is the same as the soil size-grade distribution characteristics of the middle Heihe River basin [27], also reflecting poor soil texture in arid regions. Increasing decomposition of vegetation and inputs of organic matter after reclamation were responsible for the improved soil nutrients. Bouyoucos indicated that an increase in organic matter improved the soil moisture content [28], and organic matter provides the cementation for water-stable soil aggregates [29]. The soil porosity of original sand improved with the increasing trend of organic matter, silt contents and clay contents, thus improving the soil structure.

5 Summary With farmland being the main land use, as a result of population and economic pressures, there are important and positive impacts on the soil environment during the process of expansion into the oasis–desert ecotone. The contrastive analysis of physical and chemical properties (e.g. soil moisture, nutrient and particle sizes) between a 15-y-cultivated farmland plot and a control plot in the ecotone was helpful to obtain a preliminary understanding of variation in soil environmental characteristics in the oasis–desert ecotone. There was a significant difference in soil moisture of each layer between farmland and control plots within depths of 0–100 cm due to irrigation. The soil moisture of each layer of the farmland plot during the farming period (i.e. April–September) was generally higher than that of the same layer of the control plot. The agricultural soil moisture showed a time-variation rule from multimodal to unimodal with increased depth. The soil moisture of the control plot showed a generally monotonic increasing trend with increased depth. However, the farmland plot showed a unimodal increasing trend of initial increase and then a decrease with increased depth, with the peak value at 50–60 cm during July–September and at 60–80 cm during other months. Under the preconditions of inputs of certain substances and energy, soil nutrient conditions of farmland were obviously improved, and the soil nutrient index was significantly higher than that of the control plot. The improving effect of farmland reclamation on soil nutrient conditions in the oasis–desert ecotone was limited however, and such an improving effect decreased with increased depth. For both farmland and control plots, the soil nutrient composite indexes at 0–20 cm were clearly higher than those of other layers within 20–100 cm; but the soil nutrient content in both farmland and control plot in oasis–desert ecotone is not high compared with other regions in China. Cultivation and management had a positive impact on soil particle-size distribution in the oasis–desert ecotone: silt contents and clay contents in the farmland soil obviously increased while sand contents decreased. Acknowledgements. The project was supported by the National Basic Research Program of China (973 program 2009CB421302), Technology Key Project of Xinjiang (Grant No.200733144-2), The National Science and Technology Supporting Program of China (2009BAC54B01) and National Natural Science Foundation of China


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(NO.41001171). The authors also thank the anonymous reviewers for their valuable comments.

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Chlorimuronethyl Resistance Selectable Marker Unsuited for the Transformation of Rice Blast Fungus (Magnaporthe Grisea) Chang Qing*, Yang Jing*, Liu Lin, Su Yuan, Li Jinbin, Zhu Youyong, and Li Chengyun** Key Laboratory of Agro-biodiversity and Pest Management of the Education Ministry of China, Yunnan Agricultural University, Kunming, 650201, China [email protected]

Abstract. Chlorimuronethyl resistance gene is increasingly used as a selectable marker for transformation, especially fungal transformation. Magnaporthe grisea is an important model organism for investigating fungal pathogenicity, and Agrobacterium tumefaciens-mediated transformation (ATMT) is used for functional mutagenesis of the fungus. However, our results showed that rice blast strains collected from infectious rice fields have highly conserved resistance to chlorimuronethyl, even comparable to transformants which carrying chlorimuronethyl resistance genes as selectable marker in laboratory conditions. PCR results showed that all tested field strains presented the amplified products of the same size as the selectable marker amplified from plasmid carrying chlorimuronethyl gene. Sequence analysis of PCR products amplified from field strains confirmed that field strains harbored the highly identity homolog of chlorimuronethyl resistance gene. Blast search in GenBank suggested that the fragment is presenting in reference genome sequence of 70-15, but it is not a wide-spread gene in other organisms, excepted for Herpetosiphon aurantiacus. Although the origin and reason of the conserved chlorimuronethyl resistance gene in field isolates of blast fungus is unclear, the ecological function of the gene is noteworthy. Keywords: Magnaporthe grisea, chlorimuronethyl resistance gene, selectable marker, fungi transformation.

1 Introduction Fungi play important roles in many human, plant, and animal activities, including biotechnological processes, phytopathological and biomedical research. They are also excellent models for molecular and genetic studies (Casas-Flores et al., 2004). Molecular studies of fungal biology have been greatly advanced by Agrobacterium tumefaciens-mediated transfromation (ATMT) techniques. Transformation via non-homologous integration of * The authors make equal contributions to the paper. ** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 335–342, 2011. © IFIP International Federation for Information Processing 2011

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plasmid DNA carrying a selectable marker has been widely used for fungal transformation (Chang et al., 2006). For comprehensive and in-depth study of the interaction between pathogen and host, a series of vectors bearing more available selectable markers (e.g. more antibiotic resistant genes) must be constantly developed to meet ATMT. Magnaporthe grisea has been extensively utilized as a model of fungal pathogen for understanding the molecular basis of host plant-fungus interaction, due to its genetic and molecular tractability (Dean, 1997; Talbot, 1995), as well as the economical importance of the disease it caused. ATMT have been widely used to investigate the infection process and the genes involved in the complex interaction between M. grisea and rice. To date, many selectable markers have been used for fungal transformation, such as hygromycin, bialaphos, zeocin and chlorimuronethyl resistance genes. Based on current reports, chlorimuronethyl has not yet been reported to be used for M. grisea transformation, but other selective markers have been used for the purpose. Chlorimuronethyl belonged to the sulfonylureas series herbicide. The chlorimuronethyl resistance gene has been engineered to be modified and eliminate sites for the most common restriction enzymes, and chlorimuronethyl selectable markers have been used to construct a series of vectors for fungal transformation (Sweigard et al., 1997). Sulfonylureas, especially chlorimuronethyl, which was the active ingredient of the herbicide Classicreg, inhibits acetolactate synthase (Sweigard et al., 1997). The sulfonylurea resistant allele of M. grisea ILV1 has been subcloned as a 2.8 kb fragment and modified by the elimination of eight enzyme sites (Sweigard et al., 1997). The chlorimuronethyl resistance gene that was cloned from allele of the Magnaporthe grisea ILV1 encoded acetolactate synthase involved isoleucine and valine synthesis (Sweigard et al., 1997). ILV1 was a homologue of MGG_07224 (threonine dehydratase) from Magnaporthe grisea 70-15 (Hoffmann and Valencia, 2004). Chlorimuronethyl selectable markers was used for Neurospora crassa (Li et al., 2005), Cercospora nicotianae (Chen et al., 2007) and other fungal transformations. Although M. grisea transformation has many available selectable markers, in some special cases, other selectable markers such as the marker bearing the chlorimuronethyl resistance gene has been developed to cater for special requirements. This begs the question whether this selectable marker could be successfully used for M. grisea transformation. In the present study, the chlorimuronethyl resistance gene has been studied as a potentially available marker for M. grisea transformation.

2 Materials and Methods 2.1 Fungal Strain and Cultural Conditions Thirty field isolates and two transformants of M. grisea were used in this study. Field field isolates were collected from different regions of Yunnan Province and the two transformants were obtained through Y98-16 transformation. pBIMgNIP04 was constructed plasmid bearing the chlorimuronethyl resistance gene. Fungal cultures were grown on oatmeal agar (OMA; 40 g of oatmeal for 1 L) at 25°C under continuous fluorescent light to promote conidiation (Lee and Lee, 1998). Conidia were harvested from 7- to 10-day old cultures using sterilized water.

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2.2 Transformation

℃

The A. tumefaciens strain GV3101, containing pBIMgNIP04, was grown at 28 for 48 h in a minimal medium (MM; Hooykaas et al., 1979) supplemented with kanamycin (50 μg/ml). Bacterial cells in a 2 ml aliquot of this culture were harvested and washed with an induction medium (IM; Bundock et al., 1995). The cells were resuspended with 5 ml of IM containing 200 μM acetosyringone (AS). The cells were grown for an additional 4-6 h before mixing with an equal volume of conidial suspension of M. grisea field strain Y98-16. This mix (200 μl) was plated on filter paper on a co-cultivation medium, adding 200 μM AS. Following co-cultivation at 25°C for 72 h, the filter paper was cut into strips using scissors, and they were reversely plated on PDA medium plates containing chlorimuronethyl (purchased from Japan, CAS: 9082-32-4, No.036-16671) as the selection agent for fungal transformants. Concentrations of chlorimuronethyl were 100, 200 and 300 μg/ml. In order to eliminate the remaining A. tumefaciens cells, the cefotaxime (200 μg/ml) and spectinomycin (200 μg/ml) were added in the PDA medium. After 3-day old selective cultivation, individual transformants were transferred into OMA medium containing chlorimuronethyl (300 μg/ml) and incubated until conidiation. Conidia of the individual transformants were picked and transferred to OMA. 2.3 DNA Extraction and PCR Determination Based on chlorimuronethyl resistance gene nucleotide sequence, PCR primer pairs were designed by software Primer Designer. The primer sequences followed as 5 ' - G C A A G G A G T G G T T C G A C C A G A T C A A - 3 ' and 5 ' - G T C A G A G C A T C A C C G A C A T C G T C A G - 3 ' , and the PCR product size was 562 bp. DNA was extracted

Fig. 1. PCR product of chorimuronethyl resistance gene in genomic DNA of rice blast fungus, M. grisea Lane1: DL2000 marker, lane2-9: partial rice blast strains from different fields from Yunnan, lane10: wild type strain Y98-16 genomic DNA as template; lane11: wild type strain CY2 genomic DNA as template; lane12: genetic transformant carrying chlorimuronethyl resistance gene as selection marker; lane13: negative control

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℃

from fungal mycelia grown in potato dextrose broth for 4 days at 28 at 120 rpm. The DNA extraction method followed Chadha and Gopalakrishna (2005). PCR reaction was performed on an Eppendorf PCR machine. Each tube contained a 25 µl reaction mixture, including Taq polymerase (TAKARA Biotechnology (Dalian) Co. Ltd). Thermal cycling conditions consisted of 2.5 min at 95 following by 35 cycles of 30 s at 94 and 30 s at 62 , 1 min at 72 , and one final cycle of 10 min at 72 .

℃

℃

℃

℃

℃

3 Results PCR results showed that all tested strains, including the two transformants, were expected to appear at ~500 bp band (Fig. 1). To confirm whether the PCR products were identical, the PCR product from wild type strain Y98-16 and one transformant MgNIP04-1 bearing the chlorimuronethyl resistance gene were cloned into pGEM-T

Fig. 2. Sequence alignment of chlorimuronethyl resistant gene with M. grisea 70-15, transformant carrying chlorimuronethyl resistance gene and wild type strain of Y98-16. The aligned sequences of transformant and Y98-16 were cloned and sequenced from PCR products that using primer pairs of chlorimuronethyl resistant gene to amplify transformant DNA and Y98-16 DNA, respectively.


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vector and sequenced. The sequencing results were aligned with 70-15 genome sequences and partial coding sequence of chlorimuronethyl resistance gene using BioEdit (Fig. 2). Based on alignment results, a section of DNA sequences, which located in supercontig 6.18, ranging from 1272316 to 1275124 of 70-15 strains, were homologous with the chlorimuronethyl resistance gene and PCR product from Y98-16. For all aligned sequences, almost all of bases were identical.

Fig. 3. Chlorimuronethyl resistance test of wild type strains of CY2 and Y98-16 and two transformants of T1 and T2. CK (control): no chlorimuronethyl; 100 µg/ml mean concentration of chlorimuronethyl was 100 µg/ml; 200 µg/ml mean concentration of chlorimuronethyl was 200 µg/ml; 300 µg/ml mean concentration of chlorimuronethyl was 300 µg/ml.

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Chlorimuronethyl resistance gene sequence was blasted in NCBI, the result showed that the gene appeared higher identity with M. grisea acetolactate synthase gene and partial coding sequence of MGG06868 (E-value: 0.0), while appeared lower identity with Herpetosiphon aurantiacus ATCC 23779 (E-value: 4e-05), but it is absent in other organisms presented database, suggested that it is suitable marker for transformation of other fungi and organisms. To understand the genomic environment of the gene, sequences located upstream and downstream of the gene was carried out. There was no any transponson around the gene. G+C% content of M. grisea homologue to chlorimuronethyl gene, -1818 bp of 5’ and 3’ terminal of the homologue was analyzed using Seqool, respectively. The result showed that GC% content of the homologue was 52.96%, -1818bp of 5’ terminal was 55.23%, and -1818bp of 3’ terminal was 47.47%. Based on the concentration of chlorimuronethyl introduced by Sweigard (1998), which was 100 μg/ml, it was necessary to verify whether the wild type strains were resistant to chlorimuronethyl under different concentrations. The transformant Y98-16 and CY2 were screened on PDA medium plates containing chlorimuronethyl (100, 200 and 300 μg/ml, respectively) as the selection agent. Results showed that all test strains, including Y98-16 and CY2, showed resistance to chlorimuronethyl in the tested concentrations (100, 200 and 300 μg/ml) (Fig 3).

4 Discussion Fungal genetic transformation has greatly accelerated the analysis of gene function. Fungal transformation methods include protoplast, biolistic and agrobacterium tumefaciens-mediated transformations (ATMT). And Agrobacterium tumefaciens-mediated transformation (ATMT) is used for functional mutagenesis of the fungus(Jeon and Lee et al, 2007). No matter which methods are used, there must be antibiotic resistance genes as selectable markers for successfully selecting incorporated genes for a desired trait during transformation. For transformants to be successfully screened, the plasmid containing an antibiotic resistance gene must be constructed. There are four types of selectable marker genes, including antibiotic resistant (e.g. neomycin and kanamycin), herbicide tolerant (e.g. bialaphos and chlorimuronethyl), metabolic/auxotrophic and screenable marker genes (www.nuffieldfoundation.org/ bioethics/publication/modifiedcrops/rep0007969.html). Of these selectable markers, herbicide tolerant marker was originally used in screening transgenic plants, but now this marker was also used for fungal transformation. Since green evolution, quantities of herbicides have been used to control grasses. Some grasses had developed tolerance to these chemical components during long-term competition with chemicals. While chlorimuronethyl as for a kind of herbicide was applied in fields in early 1980s and it widely applied in rice fields, soybean fields, maize fields, wheat crop fields, rape fields, lawn and other weeds in non-cultivated land for a long time. In addition, increasing transgenic organisms carrying herbicide-resistance genes such as chlorimuronethyl and bialaphos were released into the fields, and possibility of gene flow from these transgenic organisms to other organisms, especially under herbicide stress is present. If a specific DNA sequence of a strain had


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higher or lower G+C content than its genome mean G+C content, or up- and down-stream sequence, which indicated that this specific DNA sequence was obtained from exogenous bacterium or plasmid of other species (Li et al, 2008). The GC content of M. grisea homologue to chlorimuronethyl gene was analyzed, the result showed that GC content of the homologue and its 5’ terminal were higher than M. grisea genome (GC% of content was 51.57%), and its 3’terminal was lower than 51.57%. which offered a speculation that M. grisea homologue to chlorimuronethyl gene possibly was obtained from exogenous bacterium or plasmid of other species. It was necessary to verify the speculation through experimental method. So, the selectable marker carrying chlorimuronethyl gene was unsuitable for M. grisea genetic transformation. If transgenic plants or fungi carrying Chlorimuronethyl were released into fields or markets, it would inevitably threaten human and/or animal health. Therefore, it is necessary to develop suitable and safe selectable markers in the future. Crop developers have been seeking more useful markers for selecting transgenic plants, animal or fungi and these methods have been adopted in the selection process (Dale and Ow, 1991; Ebinuma, et al., 1997). In conclusion, some markers carrying antibiotic genes such as chlorimuronethyl, were neither suitable for M. grisea transformation nor other fungi or plant transformation from the long-term perspective of global food and environmental safety.

Acknowledgements We thank Dr M.A. Fullen for helpful suggestions and manuscript modification. This work is partially supported by the National Basic Research Program (2006BC100202) and the National Natural Foundation (30860161).

References Bundock, P., den Dulk-Ras, A., Beijersbergen, A., Hooykaas, P.J.J.: Trans-kingdom T-DNA transfer from Agrobacterium tumefaciens to Saccharomyces cerevisiae. EMBO J. 14, 3206–3214 (1995) Casas-Flores, S., Rosales-Saavedra, T., Herrera-Estrella, A.: Three decades of fungal transformation: novel technologies. Methods Mol. Biol. 267, 315–325 (2004) Chadha, S., Gopalakrishna, T.: Genetic diversity of India isolates of rice blast pathogen (Magnaporthe grisea) using molecular markers. Curr. Sci. 88, 1466–1469 (2005) Chang, H.K., Park, S.Y., Rho, H.S., Lee, Y.H., Kang, S.: Filamentous fungi (Magnaporthe grisea and Fusarium oxysporum). Methods Mol. Biol. 344, 403–420 (2006) Chen, H.Q., Lee, M.H., Chung, K.: Functional characterization of three genes encoding putative oxidoreductases required for cercosporin toxin biosynthesis in the fungus Cercospora nicotianae. Microbiology 153, 2781–2790 (2007) Dale, E.C., Ow, D.W.: Gene transfer with subsequent removal of the selection gene from the host genome. Proc. Natl. Acad. Sci. USA 88, 10558–10562 (1991) Dean, R.A.: Signal pathways and appressorium morphogenesis. Annu. Rev. Phytopathol. 35, 211–234 (1997)

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Ebinuma, H., Sugita, K., Matsunaga, E., Yamakado, M.: Selection of marker-free transgenic plants using the isopentenyl transferase gene. Proc. Natl. Acad. Sci. USA. 94, 2117–2121 (1997) Hoffmann, R., Valencia, A.: A gene network for navigating the literature. Nature 36, 644 (2004) Hooykaas, P.J.J., Roobol, C., Schilperoort, R.A.: Regulation of the transfer of Ti-plasmids of Agrobacterium tumefaciens. J. Gen. Microbiol. 110, 99–109 (1979) Jeon, J., Park, S.Y., Chi, M.H., Choi, J., Park, J., Rho, H.S., Kim, S., Goh, J., Yoo, S., Choi, J., Park, J.Y., Yi, M., Yang, S., Kwon, M.J., Han, S.S., Kim, B.R., Khang, C.H., Park, B., Lim, S.E., Jung, K., Kong, S., Karunakaran, M., Oh, H.S., Kim, H., Kim, S., Park, J., Kang, S., Choi, W.B., Kang, S., Lee, Y.H.: Genome-wide functional analysis of pathogenicity genes in the rice blast fungus. Nat. Genet. 39(4), 561–565 (2007) Lee, S.C., Lee, Y.H.: Calcium/calmodulin-dependent signaling for appressorium formation in the plant pathogenic fungus Magnaporthe grisea. Mol. Cells 8, 698–704 (1998) Li, Z.J., Li, H.Q., Diao, X.M.: Methods for the identification of horizontal gene transfer (HGT) events and progress in related fields. Hereditas (Beijing) 30(9), 1108–1114 (2008) Li, D., Bobrowicz, P., Wilkinson, H.H., Ebbole, D.J.: A mitogen-activated protein kinase pathway essential for mating and contributing to vegetative growth in Neurospora crassa. Genetics 170, 1091–1104 (2005) Sweigard, J.A., Chumley, F.G., Carroll, A.M., Farrall, L., Valent, B.: A series of vectors for fungal transformation. Fungal Genet. Newsl. 44, 52–53 (1997) Sweigard, J.A., Carroll, A.M., Farrall, L., Chumley, F.G., Valent, B.: Magnaporthe grisea pathogenicity genes obtained through insertional mutagenesis. Mol. Plant-Microbe Interact. 11, 404–412 (1998) Selectable Markers. The Nuffield Foundation (May 16, 2000), http://www.nuffieldfoundation.org/bioethics/publication/ modifiedcrops/rep0007969.html Talbot, N.J.: Having a blast: exploring the pathogenicity of Magnaporthe grisea. Trends Microbiol. 3, 9–16 (1995)

Support Vector Machine to Monitor Greenhouse Plant with Gaussian Loss Function Manfu Yan1, Qing Zhang1, and Jianhang Zhang2 1

Department of Mathematics, Tangshan Teachers’ College, Tangshan Hebei 063000, China [email protected] 2 Faculty of Engineering, National University of Singapore, Singapore 119278

Abstract. In this paper, it applys Gaussian loss function instead of ε-insensitive loss function in a standard SVRM to devise a new model and a new type of support vector classification machine whose optimization problem is easier to solve and has conducted effective test on open data set in order to apply the new algorithm to environment monitoring in greenhouse plants and the monitoring result is better than any other method available. Keywords: Support Vector Machine, Gaussian Loss Function, Greenhouse Plants, Environment Monitoring.

1 Introduction It can be conclude that, the classification problem is a special type of the regression problem from the mathematics language description [1]. Therefore, it is feasible to create classification algorithm by Suport Vector Machine (SVM). A general Support Vector Regression Machine usesε-insensitive loss function to create classification algorithm. However, the solution of the problem is very difficult to get in this method. As a result, by substituting Gaussian loss function for the ε-insensitive loss function, the dual problem is derived. After some simplification and transformation of equations, the resultant optimization problem is easy to solve. At last, this new method is applied to Iris open data set, in order to do the data collection and algorithm varification. In a word, the new algorithm can be applied to modern agriculture and solve practical problems, and it is proved to be achievable and effective.

2 SVR Model of Solving Classification Problems A classification problem is that the trainingset is given as, T = {( x1 , y1 ),L , ( xl , yl )} ∈ (X × Y )l

(1)

={1,  −1}, i = 1, L , l , then it could figure out one real-valued function here xi ∈ X = R n , yi ∈ Y g ( x ) in order to deduce the corresponding value y under any mode x by using decision function D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 343–352, 2011. © IFIP International Federation for Information Processing 2011

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f ( x ) = sgn( g ( x ))

(2)

By comparing the definition with that of classification problems, the latter can be considered as a kind of special regression problem, thus it is able to solve such problems by applying Support Vector Regression Machine. 2.1 The Original Optimization Problem and the Dual Problem Now, try to consider the classification problem as regression problem since yi takes value from {1, −1} , and instead of ε-insensitive loss function, Gaussian loss function is selected, the form of original optimization problem is min w ,ξ ,b

1 C l 2 w + ∑ ξ i2 2 2 i =1

s.t (( w ⋅ xi ) + b) − yi ≤ ξi , i = 1, 2,L , l

yi − (( w ⋅ xi ) + b ) ≤ ξ i , i = 1, 2,L , l ξi ≥ 0, i = 1, 2,L , l

(3)

(4) (5) (6)

Problem (3)-(6) equal to min w,ξ , b

1 C l 2 w + ∑ ξi2 2 2 i =1

s.t ξi ≥ (( w ⋅ xi ) + b) − yi , i = 1, 2,L, l

(7)

(8)

Apparently constraints in the problem can be formulated as equalities, then problem (7) and (8) equal to min w ,b

1 C l 2 w + ∑ ( yi ( w ⋅ xi ) + b) −1) 2 2 2 i =1

(9)

Let

ηi = 1 − yi (( w ⋅ xi ) + b)

(10)

then the above problems can be expressed as min w, ξ ,b

s.t

1 C l 2 w + ∑ηi2 2 2 i =1

yi (( w ⋅ xi ) + b) = 1 − ηi , i = 1, 2,L , l

(11) (12)

The resulting problem is exactly the original optimization problem in the Least Squares Support Vector Machine.

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Next, discuss properties of the resulting problem and its dual problem, and then create an algorithm based on them. Theorem 1. The Dual problem of problem (11)-(12) is l δ 1 l l α iα j yi y j (( xi ⋅ x j ) + ij ) − ∑ α i ∑∑ C 2 i =1 j =1 i =1

min α

l

∑α y

s.t

i

i =1

i

=0

(13)

(14)

where ⎧1 i = j ⎩0 i ≠ j

δ ij = ⎨

(15)

Proof. Introducing the Lagrange function of problems (11)-(12) L( w, b,η , α ) =

1 2 C l 2 l w + ∑ηi −∑ α i ( yi (( w ⋅ xi ) + b) + ηi −1) 2 2 i =1 i =1

(16)

where α ∈ R l is the Lagrange multiplier vector, find the minimum of Lagrange function with respect to w, b,η , get the following KKT condition: l

w = ∑ α i yi xi i =1

l

∑ yα i =1

i

η=

i

=0

α C

yi (( w ⋅ xi ) + b) + ηi − 1 = 0, i = 1, 2,L , l

(17) (18) (19) (20)

Substitute the above conditions into the Lagrange function and find the maximum of α , the dual problem (13) and (14) are obtained. The several theorems below are about relations between solution of original problem (11)-(12) and that of dual problem (13)-(14) are all established: Theorem 2. The solution (w∗ , b∗ ,η ∗ ) of original problem (11)-(12) exists and the solution is unique. Theorem 3. Suppose ( w∗ , b∗ ,η ∗ ) is the solution of original problem(11)-(12), then dual problem(13)-(14) must have solution α ∗ = (α1∗ ,L , α l∗ )T to satisfy l

w∗ = ∑ α i∗ yi xi i =1

(21)

Proof. Concluded from Theorem 1 and Wolfe Theorem, if (w∗ , b∗ ,η ∗ ) is the solution of original problem (11)-(12), and the dual problem(13)-(14) must have solution which satisfies equation (21).

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Theorem 4. Suppose α ∗ = (α1∗ ,L , αl∗ )T is an arbitrarily solution of dual problems(13)-(14), then the solution to ( w, b ) of original problem (11)-(12) exists and must be unique, l

w∗ = ∑ α i∗ yi xi

(22)

i =1

b∗ = yi (1 −

α i∗ C

l

) − ∑ α ∗j y j ( x j ⋅ xi ) j =1

(23)

Its proof can be seen in [2] 2.2 The SVR Algorithm of Solving Classification Problems

For general nonlinear problems, put the input space R n into a single mapping Φ (⋅) , which can transform it to a high-dimensional Hilbert space. In this space, the original optimization problem is constructed and its dual problem is obtained. l l l δ min 1 ∑∑ ai a j yi y j (Φ ( xi ) ⋅ Φ ( x j ) + ij ) − ∑ ai α C 2 i =1 j =1 i =1

l

s.t

∑a y i

i

=0

(24) (25)

i =1

Instead of the dual problem of the inner product (Φ ( xi ) ⋅ Φ ( x j )) , the kernel function K ( xi , x j ) is introduced, then the dual problem becomes, l δ ij 1 l l ai a j yi y j ( K ( xi , x j ) + ) − ∑ ai ∑∑ 2 i =1 j =1 C i =1

min α

l

s.t

∑a y i =1

i

i

=0

(26)

(27)

δ ij

For K ( xi , x j ) + C in the objective function, it can be represented by a kernel function δ ij Kˆ ( xi , x j ) = K ( xi , x j ) + C

(28)

In Hilbert space, Theorem 2-4 are still hold for the relationship between the solution of dual problem and that of the original problem, then the formula of the solution to b* becomes b* = yi (1 −

α i* C

l

) − ∑ ai* yi K ( x j , x i ) i =1

(29)


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According to theorem 4, the following algorithm is established: Algorithm 1. The SVR Algorithm for Solving Classification Problems

(i)Assume a known trainingset T = {( x1 , y1 ),L , ( xl , xl )} ∈ (X × Y )l , here, xi ∈ X = R n , yi ∈ Y = {−1,1}, i = 1, 2,L , l (ii) Choose a suitable positive C and a kernel K ( x, x′) ; (iii) Construct the problem and find the solution of l l l min 1 ∑∑ ai a j yi y j ( K ( xi , x j ) + δ ij ) − ∑ ai α C 2 i =1 j =1 i =1

l

s.t

∑a y i =1

i

i

=0

(30)

(31)

The optimum solution is obtained a * = (a1* ,L al* )T (iv)To create decisive function l

f ( x) = sgn(∑ ai* yi K ( xi , x ) + b* )

(32)

i =1

Here b* is given by equation (29). 2.3 The Numerical Experiments

In order to verify the proposed Algorithm 1, a test was conducted on Iris data set [3]. The Iris data set is used to test the performance of classification algorithms. The data set contains the number of 150 sample points, which are divided into three categories, namely, I(Iris-setosa), II(Iris-versicolor) and III(Iris-virginica), there are 50 sample points in each type and each sample point has for properties. There are three two-class classification problems, namely, Class I and II is the positive class, and Class III is the negative class; or Class I and III is the positive class, and Class II is the negative class; or Class II and III is the positive class, and Class I is the negative class. In each of the two-class classification problems there are 150 sample points, which has been randomly assigned to training set and testing set. The training set contains 50 positive points and 25 negative points, while the testing set contains 50 positive points and 25 negative points. The trainings are conducted by using Algorithm 1 and standard C-SVM. During the training process, the RBF Kernel function is adopted for the two algorithms. The parameter C is set to be 0.1, l, 10, 100, 1000, 10000 and so on. The decisive functions gained in each training session are tested, and each testing results are recorded. Finally compute and compare the average testing accuracy, result is shown in the following table:

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Table 1. Result Comparison Table

Classification {I, II}—III {II, III}—I {I, III}—II

C − SVC

95.6% 100% 97.5%

Algorithm 1 96.1% 100% 97.2%

From the above comparison results, obviously Algorithm 1 and similar testing accuracy rate.

C − SVC

share the

3 The Application of SVR Classification Algorithm to the Environment Monitoring on the Greenhouse Plant Growth The great progress in technology has brought a serious problem to the traditional agriculture, which is far from meeting the needs of the modern social development [4]. Therefore, improvement and revolution must be done to the traditional agriculture. A new cultivation method is developed through many years’ experience, which is that people can control environmental factors so that the crops can grow in the most suitable environment. In addition, the growing seasons may be extended and the best output is gained. This agricultural mode is known as the greenhouse agriculture, or as factorial agriculture and greenhouse agriculture in developed countries. With its striking feature of being free from environmental constraint, the new agricultural mode enables the crops to grow under some pre-designed conditions is highly yielding and greatly effective. Hence it has been a trend all over the world. In a word, the research on environment monitoring is crucial, especially for the real-time environment monitoring. 3.1 Objective Conditions and Advantages of SVM-Based Environment Monitoring of Greenhouse Plants

The environmental factors for soilless greenhouse cultivation include temperature, humidity, CO2, illumination, EC, and nutrition elements. After discussing with agricultural experts, choosing l greenhouses to be measured with n factors as the measurement parameter. The i-th parameter is marked as [ x ]i , i = 1, 2,L , n , then x = ([ x]1 ,L, [ x]n )T here, xi ∈ R n , and normal greenhouse is recorded as 1, otherwise -1, then let T = {( x1 , y1 ) ,L , ( xl , yl )}

, Here [ x ]i ∈ R n , i = 1, 2,L , n , yi = 1, or − 1 It is just a trainingset of Support Vector Classification Machine. Thus the problem of environmental monitoring could be solved by SVM. (see 3.2). Nowadays, the normal data ranges for the environment monitoring of greenhouse plants are given. In practice, adjusting those parameters based on the standard. However, it is found that although all the data are in range, the problem is still come out


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sometimes. It indicates that the interval control method has a large deviation and it is not reliable. For example, as temperature is adjusted, it may interfere with the humidity, so the environment control is not effective and sometimes make it worse. The advantage of SVM is that it do not have to set up the ranges for each parameter beforehand; instead, taking into account of all the parameters together, resulting in the much improved accuracy of monitoring. Assume the output is -1, which means the current environment is not the one wanted, so adjusting the corresponding parameters in the computer until the result is 1. This operation is obviously easy and effective with the aid of computer. 3.2 The Application to Environment Monitoring of Celery Cultivation

Three years’ research on celery is conducted in Shouguang, Shandong and Fengnan, Hebei. 100 greenhouses are randomly taken as our sample and measured 15 parameters, which are suggested by agricultural experts. The parameters include temperature, humidity, illumination, and so on. A data set of 100 15-dimension vectors is obtained, at the same time, let agricultural experts determine whether the celery greenhouse is normal or not [5]. The 100 15-dimention vectors are given values of -1 and 1 according to the result is abnormal or not. In this way, the trainingset of the SVM is obtained. 3.2.1 Data Pre-retreatment From the data, it is noticed that some parameters are in a narrow range, for example, the nitrogen concentration is between 0.004 and 0.01, but some parameters are in a wide range, like illumination is between 200 and 1200 luxes. Thus, standardization of those parameters is needed. The maximum-minimum standardized method is used. For example, nitrogen [xi ] is between 0.004 and 0.01, so here uses maximum-minimum formula,

[x ] ′ = ⎛⎜⎜ [x ] − min ([x ] )⎞⎟⎟ ⎠ ⎝ j i

j i

j =1, 2,L100

j i

[ ] min ([x ] )⎞⎟⎟ ⎞⎟⎟ ⎠⎠

⎛ ⎛ ⎜ max ⎜ x j − ⎜ j =1, 2,L100 ⎜ i ⎝ ⎝

j =1, 2,L100

j i

(33)

Standardize the data set to D ′ . The data set is randomly splited into 2 subsets by the ratio of 7:3. One subset is trainingset T and the number of training point is l (here l = 70 ), the other subset is test set S , the test point is m (here, m = 30 ). The number of the positive points in training set T is T+ , that for the negative points is T− . Similarly, the number of the positive points in test set S is S + , the number of negative points is S − . The number of positive points is 74, and the number of negative points is 26. There is an imbalance between these two kinds of points. Therefore, set up penalty parameters C+ and C− for both positive and negative points when using SVM. C+ and C− could be determined by the following formula, C + = CT− / l ,

Here

C > 0,

C− = CT+ / l ,

which has been given in advance.

(34)

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3.2.2 The Choice of Model Suitable models of SVM are needed to be chosen for the above classificatory problem. Three models were choose, the first one is weighed proximal SVM model [6], and the original problem is min w ,η ,b

(

)

1 1 1 2 w + b2 + C+ ∑ηi2 + C− ∑ ηi2 2 2 yi =1 2 yi =−1

(35)

s.t. yi (( w ⋅ xi ) + b) ≥ 1 − ηi , i = 1,L , l ,

(36)

and its dual problem is min α

1 l l 1 ∑∑ αiα j yi y j ( K ( xi , x j ) + 1) + 2C 2 i =1 j =1 +

∑α yi =1

2 i

1 2C−

+

l

∑ α − ∑α

yi =−1

2 i

i =1

i

(37)

The second model is support vector machine algorithm 1, the optimization problem to be solved is (30) - (31). The third one is weighed standard SVM model [7]. The original problem is min w , b ,ξ

1 2 w + C + ∑ ξ i + C− ∑ ξ i 2 yi =1 yi =−1

(38)

s.t. yi (( w ⋅ xi ) + b) ≥ 1 − ξi , i = 1,L , l ,

(39)

ξ i ≥ 0, i = 1,L , l ,

(40)

and its dual problem is min α

l 1 l l α iα j yi y j K ( xi , x j ) − ∑ α j ∑∑ 2 i =1 j =1 j =1

l

s.t.

∑α y i =1

i

0 ≤ α i ≤ C+ ,

0 ≤ α i ≤ C− ,

i

(41)

=0

(42)

yi = 1

(43)

yi = −1

(44)

Proper parameters are going to be chosen after defining the three above models. The parameters include kernel function K ( x, x ′) and C+ , C− , and the parameters in kernel function as well. Here, the radial basis kernel function is choosen, ⎛ x − x′ K ( x, x′) = exp⎜ − ⎜ σ2 ⎝

2

Now the parameters to be determined are C+ , C− and

⎞ ⎟ ⎟ ⎠

σ.


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For each model, the best parameters is choosen by the method of lattice. In other words, the ranges of C+ and C− , are {0.1,1,10,100,1000,10000} and the ranges of σ , which is {0.1,0.2,0.5,1,2,5}. Thereby, they have constituted a group of parameters, ( C+ , C− ,σ ) . Loo deviations were calculated for each group of parameters [8]. The group with minimal Loo value is the group of best parameters ( C+ , C− , σ ) . For weighed proximal SVM model and it is (C = 10,σ = 2) ; for Algorithm 1 its group of best parameters is

(C

+

，

= 100 C− = 100, σ = 5 ) ;

for weighed standard SVM, it is

(C = 10,σ = 1).

3.2.3 Comparison of Results Substituting three groups of best parameters into the corresponding models, decisive functions are obtained, which can be used to decide the points in test set, as shown in the following table. Table 2. Result Comparison Table

Checkup Models Results

C-SVM

Algorithm 1

PSVC

Checkup Items

Result Precision Percentage of False Report Percentage of Checkup Result

90%

95%

86%

3%

0%

12.5%

66.7%

83.3%

66.7%

The Result Precision is the ratio of items of those checked correctly to all the items in the sample test set; the percentage of False Report is the ratio of false reported items to the number of real usual items; Percentage of Checkup Result is the percentage of the found real false items in all the real items. From the above experiment results, Algorithm 1 leads to the best result precision among three kinds of models.

4 Conclusion Based on the above discussion, the classification problem is treated as a special regression problem, and the ε-insensitive loss function is substitued to Guassian loss function, so that the optimization problem is easy to solve. This classification algorithm introduces a new way of solving classification problem, and the new algorithm has been applied to practical greenhouse plant environment monitoring. It not only solves the practical problem, but the effectiveness is varified and comparison with old method shows the advantages of the new method.

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References [1] [2] [3] [4] [5] [6] [7] [8]

Deng, N., Tian, Y.: Support Vector Machine – Theory, Algorithm and Expansion, pp. 63–64. Science Press, Beijing (2009) Yan, M.: Support Vector Machines for Classification and Its Application. China Agricultural University, Beijing (2005) (Doctor’s Degree Paper) Deng, N., Tian, Y.: Optimal Method of Data Processing – Support Vector Machine. Science Press, Beijing (2004) Wang, X.: The Problems and solutions of Public Vegetable Base, vol. (3), pp. 1–3 (2010) Zhang, D.: Celery. China Celery (1), 15 (2010) Fung, G., Magansarian, O.L.: Proximal Support Vector Machine Classification. In: KDD 2001, San Francisco, CA USA (2001) Yang, Z., Liu, G.: Principle and Application of Uncertain Support Vector Machine, pp. 148–151. Science Press, Beijing (2007) Vapnik, V., Chapelle, O.: Bounds on Error Expectation for Support Vector Machines. Neural Computation 12(9) (2000)

Classification Methods of Remote Sensing Image Based on Decision Tree Technologies Lihua Jiang1,2, Wensheng Wang1,2, Xiaorong Yang1,2, Nengfu Xie1,2, and Youping Cheng3 1

Agriculture Information Institute, Chinese Academy of Agriculture Sciences, Beijing, 100081, China 2 Key Laboratory of Digital Agricultural Early-warning Technology, Agriculture Information Institute, Chinese Academy of Agriculture Sciences, Beijing, 100081 3 Agriculture Bureau, Huailai County, Hebei Province, 075400, China {jianglh,wangwsh,yxr,nf.xie,youping}@caas.net.cn

Abstract. Decision tree classification algorithms have significant potential for remote sensing data classification. This paper advances to adopt decision tree technologies to classify remote sensing images. First, this paper discussed the algorithms structure and the algorithms theory of decision tree. Second, C4.5 basic theory and boosting technology are explained. The decision tree technologies have several advantages for remote sensing application by virtue of their relatively simple, explicit and intuitive classification structure. Keywords: Decision tree; Classification; Remote sensing image.

1 Introduction Classification and Extraction of remote sensing information has been an important content in remote sensing technology field. In remote sensing classification application, traditional classification methods [1] such as supervised classification and unsupervised classification and artificial neural nets classification [2] and expert system classification are both based on spectral image features. But because image self has the phenomenon that the same thing has different spectrum, and different things have the same spectrum, the classification methods that only rely on ground spectrum features always turn up many misclassifications and omission errors [3]. Lots of study indicates that classifications combined with image spectrum information and other assistant information can improve precision of classification largely. Decision tree classification as spatial data mining and knowledge discovery [4] supervised classification method, breaks through the problem that construction of previous classification tree or classification rule always take advantage of ecology and remote sensing knowledge ex-ante certainty and the results always closely related with experience and professional knowledge [5]. It obtains classification rules by means of decision study process and needn’t satisfy normal distribution. It can use earth knowledge in GIS database to help classify and improves precision of classify. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 353–358, 2011. © IFIP International Federation for Information Processing 2011

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At present, decision tree classification [7] has applied in remote sensing image information extraction and land utilization coverage classify. In American, USGS and EPA etc. departments have united taken out USA land coverage database plan and decision tree classification technology has not only applied in land classify but also urban density information and crown layer density information extraction. The land classify precision has reached 73%-77% and urban density information extraction precision has reached from 83% to 91% and tree crown precision has reached 78%93% [8]. Mapping efficiency has improved 50% and can satisfied with large scale land classify data production requirements. Decision tree study method is one of data mining methods to work out classify problem in practical application [9]. It can reason classify rules of decision tree form of expression. The great virtue of decision tree is that study process needn’t users know a lot of background knowledge. As long as trained examples can expressed by “property - result” and use this algorithm to learn. Classify knowledge obtained by decision tree is easy to express and apply. At present, foreigner scholars have already used decision tree to obtain knowledge and applies in spatial analysis and study process [10].

2 Decision Tree Arithmetic Decision tree is a method which can inductive learn by training samples and build up decision tree or decision rule and then use decision tree or decision rule to classify data. Decision tree is a tree construction. It is composed of a root node, a series of internal nodes and leaf nodes. Every node can have only one father node and two or more child nodes. Nodes are connected with each other by branches. Every internal node correspond a test properties or properties group and every side correspond every possible value of property. Leaf node correspond a class property value and different leaf node can correspond the same class property value. Decision tree can not only be expressed by tree, but also a team of IF-THEN production rules. Every road from root to leaf correspond one rule and the condition of rule is to option of all nodes property values. And result of rule is class property of leaf node in the road. Compared with decision properties, rules are more simple and convenient to understand, use and mend and can make up the base of expert system. So rules are used more and more in actual application. This paper mainly introduces a widely used in remote sending application arithmetic-classify and regression tree and another decision tree arithmetic C4.5. 2.1 Classify and Regression Tree (CART) Classification and regression tree is a common tree growth algorithm. It is presented by Breiman etc. [11] and it is a supervised classify. It trains the samplings to construct binary tree and decode to classify. The feature is to take advantage of the binary treestructured fully, in other words, root node includes all samplings. Root node is divided up into two child node in definite divide rules. This process repeats again in child node and becomes a regression progress until the child node can not be divided into two child nodes. Train of thought of construction a CART is that based on the

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whole sampling data, build up a multilevel and multi-leaf nodes tree to reflect relations between nodes and then cut the tree to build up a series of child trees and select appropriate tree in order to classify the data. In details, the process includes building up a tree and pruning a tree. 2.1.1 Tree Growth A discrimination of tree nodes is named a branch and it corresponds to a subset of training samples. Branch of root node corresponds to the whole training samplings. Thereafter discrimination is process of partition training samplings. So process of building up a tree is querying property to produce partition rules. In this paper, CART adopts a index called “node impurity level”: i(N) presents the impurity level of node N. When mode data in node cone from the same category, i(N)=0; when categories of data distributes evenly, i(N) will be very big. Partition rules are produces based on minimal value of impurity level function. Here two impurity functions are introduced. (1)“Entropy Impurity”, is also called information impurity:

i (N ) = −∑ P (w j )log 2 P ( w j ) j

(1)

P(w j ) is the calculus of probability accounting for w j mode sampling data that node N belonged to of the whole samplings. According to the characters of

Including,

entropy, if all mode data come from the same category, the impurity level is zero; or else impurity level is more than zero; when all categories data appears with the same calculus of probability, entropy is the maximum. (2) VAR Impurity—“Gini impurity level”.According to node samplings come from different categories and it is related with total distribution variance, below formula is put forward.

i (N ) = −∑ P (wi )P (w j ) = 1 − ∑ P 2 (w j ) i≠ j

(2)

j

Meaning of “Gini impurity level” is to represent error rate of category making in node N. When given a tree which has grown to node N and the node is attribute queried, a visible heuristic train of thought is to select the query whose impurity level drops fastest. The impurity level drops can be noted:

Δi (N ) = i (N ) − PL i (N L ) − (1 − PL )i (N R )

(3)

N L and N R are separately left node and right node; i( N L ) , i ( N R ) are separately impurity level. PL is the probability that when query T is adopted, the tree grows from N to N L . And the optimum query value S is the maximum value of Δi (T ) . Including,

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2.1.2 Tree Pruning If we persist in building up trees until all the leaf nodes reach minimal impurity level. The data will be fitted excessively and classify tree will degenerate to a convenient lookup table. It is maybe not good for noise signal lamp generalization performance of bigger Bayes error. On the contrary, if the branches stop too early, error of training sampling will be not small enough resulted in category performance is very poor. A main stopping branch method is pruning, at the same can prevent the tree growing too gigantic. In this paper, below index is adopted to reach the aim.

cos t = α • size +

∑ i(n )

leaf node

(4)

cos t is represented cost function of tree weighting error probability and complexity penalty summation. size is represented leaf node quantity to weight complexity of ∑ i(n) is represented tree classifier. α is represented complexity index. leaf node

summation of impurity level of all of leaf nodes to show the uncertainty of adopting this classification tree to classify training samplings. According to formula (4), tree pruning can be completed by below two steps: (1) In all the brother leaf nodes, compare cos t after combined leaf nodes. (2) Delete the leaf node that cos t reduces the most. If cos t has not reduced, nothing will be done. Repeat above pruning process until pruning can not go on. In pruning process, training error deduces with leaf nodes increasing; testing error deduces at the beginning and reaches minimum and then gradual roll up affected by training samplings. Take use of independent data to test, and select the subtree which has the minimum test error as decision tree. This paper adopts a heuristics verification technique —cross validation to select the best tree: 10-fold cross validation. The training samplings are divided into ten subsets which are equality in number and disjoint with each other. Classifier will train the data 10 times and every time nine groups data subsets are trained and the test one is as validation set to estimate testing error. Estimated testing error is the average value of the ten groups. 2.2 C4.5 Basic Theory C4.5 is another widely applied signal decision tree building up method, and is adopts information gain ratio to classifier. It uses training group to select the properties whose information capture rate is the largest and information gain in not less than all properties average value as tree nodes. Take every possible value as a branch of node and recursively builds up a decision tree. Entropy impurity level function in CART is adopted in building up a tree. The information gain is equivalent to “impurity level decreasing value” in CART. In addition, index of capture rate is added in order to wipe off influence of high branch property. At the same time, capture rate take leaf node count and size of every node after every partition into account. Consideration objects mainly are every partition but not information content in category. Termination

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condition is that properties of records in subset are the same or no property can be divided. The difference compared with CART, C4.5 take advantage of statistical significant error probability technique based on branches to realize pruning. Another significant difference is that processing method to damage pattern. In training period, C4.5 has not adopted surrogate split to settle damage of categorization data, but adopts probability weighting method to deal with “property missing”. 2.3 A New Technique Adopted in Decision Tree—Boosting Method In decision tree classifier designing, a boosting technology is widely used in the middle period of 1990s in machine learning field to improve classifier precision. This method can boost samplings classifier precision which is difficult to recognize. At the same time, this technology can cut down sensitivity that classifier algorithm affecting data noise and training sampling error. Boosting is a learning method which can boost any learning algorithm precision and it can boost weak learn algorithm to strong learn algorithm. Its theory comes from probably approximately correct learning model. It can take advantage of some learning algorithm to generate a series of base classifiers. Every base classify training depends on classifier results produced by former classifier and endows failed training samplings with major weight to pay them more attention in subsequent learning. At last, classifier weights voting every base classifier and gets the last result and reduces signal classifier error and improve classify precision. Freund and Schapire brought forward the most pragmatic boosting algorithm—AdaBoost according to boosting basic theory in 1995 and widely applied.

3 Conclusion The advantage of decision tree algorithm used in remote sensing data classify lies in that it can show the shortage of MLC algorithm when deals with complicated distribution data sets. Decision tree has better flexibility and robustness for data distribution feature and classify marking. So when remote sensing image data features distribution is very complicated or dimensions of source data have different statistical distribution and scales, decision tree classify method can obtain the best classified results. Tree classify construction of decision classify method need not suppose some sort of parametric density distribution in advance. So the whole classify precision is superior to traditional parametric statistics classify method. But with the development of artificial intelligence technology and theory, study of remote sensing image classify has developed to a higher level. Geonomy knowledge and aid decision making of geographic information can boost precision of remote sensing image classification and information extraction and expert system is a good means to resolve this problem. So combination of decision tree and expert system based on knowledge is becoming a cause for concern.

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Acknowledgements This work is supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), Special fund project for Basic Science Research Business Fee, AIIS(Grant No. 2010-J).

References 1. Li, S., Ding, S.: Decision Tree Classify Method and Application in Earth Coverage Classify. Remote Sensing Technology and Application 17(1), 6–11 (2002) 2. Luo, L., Gong, H.: Study and Implement of Remote Sensing Image Decision Tree Classifier. Remote Sensing Information, 13–16 (2006) 3. Li, F., Li, M.: Remote Sensing Image Auto Classify Study Based on Combination of Artificial Neural Networks and Decision Tree. Remote Sensing Information 3, 3–25 (2003) 4. Jiang, Q., Liu, H.: Use Texture Analysis to Extract TM Image Information. Remote Sensing Journal 8(5), 458–464 (2004) 5. Friedl, M.A., Brodley, C.E., Strahler, A.H.: Maximizing land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales. IEEE Transactions on Geoscience and Remote Sensing 37(2), 969–977 (1999) 6. Di, K., Li, D., Li, D.: Remote Sensing Image Classify Study Based on Spatial Data Mining. Wuhan Technical University of Surveying and Mapping Journal 125(1), 42–48 (2000) 7. Mclver, D.K., Friedl, M.A.: Estimating Pixel-scale land Cover Classification Confidence Using Non-parametric Machine Learning Methods. IEEE Transaction on Geo-science and Remote Sensing 39, 1959–1968 (2001) 8. Mclver, D.K., Friedl, M.A.: Using Prior Probabilities in Decision-tree Remotely Sensed Data. Remote Sensing of Environment 81, 253–261 (2002) 9. Zhan, X., Sohlberg, R.A., Townshend, J.R.G.: Detection of Land Cover Changes Using MODIS 250 m Data. Remote Sensing of Environment 83, 336–350 (2002) 10. Rogan, J., Franklin, J., Roberts, D.A.: A Comparison of Methods of Monitoring Multitemporal Vegetation Change Using Thematic Mapping Imagery. Remote Sensing of Environment 80(1), 143–156 (2002) 11. Li, S., Zhang, E.: Remote Sensing Image Classify Method Study Based on Decision Tree. Territory Study and Development 22(1), 17–21 (2003)

Computer-Aided Design System Development of Fixed Water Distribution of Pipe Irrigation System Mingyao Zhou∗, Susheng Wang, Zhen Zhang, and Lidong Chen College of Hydraulic Science and Engineering, Yangzhou University, 31 middle Jiangyang Rord, Yangzhou 225009, Jiangsu Province, P.R. China Tel.: +86-514-87978640; Fax: +86-514-87978640 [email protected]

Abstract. It is necessary to research a cheap and simple fixed water distribution device according to the current situation of the technology of low-pressure pipe irrigation. This article proposed a fixed water distribution device with round table based on the analysis of the hydraulic characteristics of low-pressure pipe irrigation systems. The simulation of FLUENT and GAMBIT software conducted that the flow of this structure was steady with a low head loss comparing to other types of devices. In order to improve the design efficiency, a program was made using Visual Basic. The system was user-friendly, flexible operation, convenient and able to meet the needs of different users. Keywords: pipeline; irrigation; fixed water distribution device; ComputerAided.

1 Introduction As the economy developed speedy, the contradiction between the water use of industry, agriculture and life will be more prominent. So developing water saving agriculture comes to be an important measure to the contradiction and to improve the grain yield (Department of Rural Water Resources in Ministry of water resources, 1998; Yuanhua Li et al., 1999; Ligui Xie et al., 2001). The low-pressure pipeline irrigation system is a new water saving and energy saving irrigation system in our country these years. It proved to be saving water more than 40%, energy 20~30%, and land 2~4%. With the significant benefits and broad prospect, the low-pressure pipeline irrigation has been becoming the major trends of water saving irrigation project(Department of Science and Education in Ministry of water resources, 1991). Water gaging equipment and technology is the basic measure to plan the water using and to control the irrigation quality. It can not make the water arrangement of every plot accurate without water gaging equipment, though the recent water distribution devices have the control ability. So developing the fixed water distribution device of the pipeline system is necessary to adapt to the field irrigation management, and provide instantly accurate water allocation(Shuangen Yu et al., 2004). Water ∗



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distribution device contains tee, standpipe and hydrant, but the research on fixed water distribution device of pipeline is still relatively few(Xiao Li et al., 1996; Qingfeng Ji et al., 2001; Changde Wang, 2005). Considering the economic, reasonable and operational factors, this article discussed the fixed water distribution device with round table based on the comparison and analysis of current hydrant(Qingseng He et al., 1992; Jiesheng Huang et al., 1998; Zhengrong Huang et al., 2001; Liguo Ming et al., 2002).

2 Structure Design of Fixed Water Distribution Device with Round Table 2.1 The Principle of Operation From the comparison and analysis, we chose the adjustable fixed water distribution device with spring structure. The structure is shown in Fig.1.

Fig. 1. Fixed water distribution device with round table 1-shell; 2-spring; 3-ball valve; 4-ball bar; 5-butterfly valve

The bottom of the water distribution device is controlled by butterfly valve. When the pipeline works, the butterfly valve is opened, and the spring becomes deformed under the impulse of water flow. The deformation is larger as the water pressure is higher, and the ball goes to the upper part of the device. For the special structure of the round table, the area of flow comes down and the flow rate stays steady. And vice versa. 2.2 Design of the Structure Dimension Because of the water impulse force, the ball will be at different places. If we want the flow rate maintain steady, the area of flow should be corresponding to the water pressure. This can be put into practice by the structure of round table. The structure dimension is shown in Fig.2.

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In Fig.2, r1 is the radius of the ball, r and R is the radius of the top and the bottom of the round table, R1 is the radius at the position of flow area, and h is height of the round table. The structure dimension design can be divided into three steps:

Fig. 2. Structure dimension of the device

First, set the ball radius, then calculate R1 by (1).

R1 =

A

π

+ r1

.

(1)

Second, make certain the height of the round table combining the standpipe. Finally, calculate r and R. It should be in the standard pipe size, in order that it is propitious to manufacture and install. In additional, the top and bottom of the round table need to meet the conditions as follows: (1)The area of the top is greater than the minimal flow area of the device. (2)There is a differential between the area of the top and the bottom, so the flow area can change as the ball moving. (3)There should not be a huge difference between the radius of the bottom and the standpipe, or it is easy to damage and hard to install. 2.3 Design of the Spring It made a simple treatment when design the spring. The spring was thought to be a uniform elastic rod and it only did one-dimensional longitudinal vibration(Zhilun Xu, 2002). When the spring interacted to other objects, it followed the Hooke’s law. So Hooke’s law became the starting point of the spring problem. The analysis of the ball’s force balance is shown in Fig.3.

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Fig. 3. Force analysis of the ball

In Fig.3, F1 is the water impulse force, F2 is the elastic force, and G is the ball’s gravity.

F1 = F2 + G .

(2)

F2 = ρQβ v + πP1 r12 − G .

(3)

In the formula, Q is the runoff of the device, m3/s; ρ is the density of water, kg/m3; V is the flow rate, m/s; P1 is hydrodynamic pressure, pa. The elasticity of the spring can be ascertained by the Hooke's Law F = k x.

·

F2 N − F21 ρQβv N + πPN r1 − ρQβv1 − πP1r1 = x xmax . 2

k=

2

(4)

3 Flow Field Simulation of Fixed Water Distribution Device The structure can be improved by flow field analysis using FLUENT software. At the same time, we can compare the advantages and disadvantages with the other structures. 3.1 Simulation of the Flow Field (1)Build the model by GAMBIT GAMBIT is a high-quality pre-processor for CFD analysis which can be used to build models and generate grids. Before the CFD simulation, draw the grid figure and the boundary nodes by GAMBIT, then structured the grids, set boundary type and save the grids. (2)Simulation the flow by FLUENT Start the FLUENT 2D solver, read the grids file, and ascertain the unit length. Set the fluid physical properties and boundary conditions, use the standard κ ε onflow model and non-coupled solution method to solve the steady flow of two-dimensional space(Fujun Wang, 2005; Hongwei Wang et al.,2009).

－


(a)

(b)

(c)

(d)

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（a）Sliding water distribution device （b）Ball valve water distribution device （c）Gland water distribution device （d）Plate valve water distribution device Fig. 4. Comparative analysis of flow field

3.2 Simulation Conclusion From the Fig.4, we can conclude that the ball valve water distribution device with round table had a more steady flow. The flow rate of this structure changed slightly and the fluid state was pretty well. There were many swirls in the other three structures and the flow was disordered which could not fill the pipe. We can obtain some conclusions through the simulation result: (1)Arc-shaped bend pipe was more favorable than right angle bend pipe for the water flow steady through and keeping a stable flow field. (2) The round table was a bundle mouth structure, which was more suitable for the fluid flow. And that played a role of steadying flow diversion, ensured the flow rate changed little, and reduced the swirl generation. (3) The ball valve measured up to the law of liquid flow, didn’t hinder the water flow. The flow can keep their original streamline with few swirls and turbulence.

4 Computer Aided Design System of Fixed Water Distribution Device In order to improve the design efficiency, we compiled a design program of fixed water distribution device using Visual Basic language to meet the different irrigation conditions and different users.

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4.1 System Development Process When develop a new system, we should confirm the objectives, clients and implementations first, and make the system intuitive with friendly interface, operability and flexibility. Therefore, it should make sure the development process of the system. (1) Fixed the arrangement of the pipeline system, the distance between the water distribution devices, the device number, the pipe diameter, the runoff and other key factors. (2) Solved the head loss of pipeline. n

h = 1.1∑ 0.948 ×10 5 × i =1

(nq)1.77 × n× L d 4.77 .

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In the formula, d, q, l, i were separately the pipe diameter, single device flowrate, the distance between the water distribution devices and the device number. (3) Solved the flow rate of every water distribution device.

H n = H n −1 + h .

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Vn = 2 gH n .

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Hn is the head at the calculated device, and Vn is flow rate. (4) Calculated the area needed for every device when they had the same flowrate.

An = q / Vn .

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(5) Set the structure dimension of the device. (6) Ascertained the position of the ball in the device. For obtaining the flow area, the ball moving distance x was needed.

xn = h

AN / π + r1 - r R −r

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(7) Analyzed the force in the ball.

Fn = ρqVn + πH n r12 .

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(8) Calculated the elastic coefficient.

k=

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In the system, L, n, d , q and r1 were the parameters that needed to be input.

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4.2 Operation System Design We programmed the design process by Visual Basic language through the analysis above. The operation interface is shown in Fig.5.

Fig. 5. Operation interface of the computer aided system

In the main program interface, input the parameters, then click the calculate button, the device dimension and the elastic coefficient of the spring will be obtained. The operation is convenient, and the program is easy to maintain and manage. Further more, the program has the ability of extension for adding the other design modules in case it is needed.

5 Conclusions (1)The structure of round table had a steady flow and low head loss proved by the flow simulation. It satisfied the design demand and adapted to the fixed distribution of pipe irrigation. (2)The spring is the main part of the round table device, and there will be a problem with the accuracy of the device when the spring was rusted. So the structure still needs to be optimized and improved.

Acknowledgements This research was funded by National key Technology R (accession number 2006BAD11B03-02).

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References Department of Rural Water Resources in Ministry of water resources: Engineering of pipe transmission, pp. 101–123. China water Power Press (1998) (in Chinese) Li, Y.: Theory and technology of water saving irrigation, pp. 45–50. Wu Han Water and Hydropower University Press (1999) (in Chinese) Xie, L., Wang, Y., Xie, Z.: Research on field engineering complement of low-pressure pipeline irrigation. Water Saving Irrigation (3) (2001) (in Chinese) Department of Science and Education in Ministry of water resources: Transmission and irrigation technology of low-pressure pipeline, 63–72 (1991) (in Chinese) Yu, S., Zuo, X., Zhao, W.: Water gaging status and development trend of irrigation district of china. Water Saving Irrigation (4) (2004) (in Chinese) Li, X., Sun, F., Zhang, L.: The pipe material and fittings of pipeline irrigation system, vol. (2). Science Press (1996) (in Chinese) Ji, Q., Shen, B., Li, G.: Research development progress of water gaging device. Irrigation and Drainage (12) (2001) (in Chinese) Wang, C.: Application of irrigation water gaging technology of china. China Water Conservation (7) (2005) (in Chinese) He, Q., Li, Z., Li, C.: Research on multifunction water distribution valve of low-pressure pipeline. China Rural Water and Hydropower, 24–28 (1992) (in Chinese) Huang, Z., Zhang, Z.: Simulation and study of auto-hydrant irrigation system. Irrigation and Drainage (4) (2001) (in Chinese) Ming, L., Xu, Q., et al.: Application research of a new autogenous pressure hydrant. China Rural Water and Hydropower (6) (2002) (in Chinese) Huang, J., Sheng, K., Zhang, Y.: A new irrigation device of paddy field—auto-hydrant. China Rural Water and Hydropower (8) (1998) (in Chinese) Xu, Z.: Concise guide of elastic mechanics, vol. 8. China Higher Education Press (2002) (in Chinese) Wang, F.: Application of CFD in the turbulence analysis and performance prediction of hydraulic machinery. Journal of China Agricultural University 10(4) (2005) (in Chinese) Wang, H., Liu, X., Liu, D.: CFD analysis of ball check valve. Fluid Transmission and Control (2) (2009) (in Chinese)

Construction and Practice of Information Demonstration Area in Mentougou District of Beijing Juan Pan, Na Zhang∗, Shan Yao, and Jian Xu Department of Computer and Information Engineering, Beijing University of Agriculture, Beijing, P.R. China 102206 [email protected]

Abstract. The rural informatization is one of the important foundation for the construction of the metropolis-modern agriculture, which Beijing government makes great effort to develop now. Based on current situation of rural informatization construction in Mentougou district of Beijing, this study established an information demonstration area in order to integrate the information from the local natural ecology, agricultural production, special products trading and government. We made use of the technology of 3S, database and network to achieve the digitalization and visualization of the rural information. The study helps to guide the agricultural production and agricultural products circulation and offers the effective decision support for the sustainable development of the demonstration area. Keywords: rural informatization, information demonstration area, 3S, information service platform, Eco-Agriculture.

1 Introduction With the balance development of modern rural and urban areas, Beijing strengthens the rural informatization construction. The rural information infrastructure in Mentougou district has made remarkable progress after years of effort. An integrated basic information service network has been established, which provides a strong guarantee for Mentougou informatization construction (Shi 2009). 3S and Internet technology provide a new mode for the management of rural information, which realizes the management of spatial information that can’t be carried out in the traditional one. The Commission of Science and Technology of Mentougou district has established three websites successively—High-quality Goods Website, Chinese Walnut Website and Ecology Commercial City Website. However, the first two websites do not have the background system, and the third one needs to improve its trading function. Furthermore, these three websites provide some redundant function, and their network systems are instable. According to the current situation of informatization construction in Mentougou district, the study established information demonstration area in ∗



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order to strengthen coordination and integration of rural information resources, and effectively promote the development of information technology in rural areas. We made use of 3S, database and network technology to manage and develop all kinds of rural information. Through the digitalization and visualization of the rural information, the study helps to guide the agricultural production and agricultural products circulation and offers the effective decision support for the sustainable development of the demonstration area.

2 The Overview of Study Area Mentougou district is located in the southwest of Beijing, 62 kilometers east-west, 34 kilometers north-south, with a total area of 1,455 square kilometers. It is located at 115 °25′ 00″ ~ 116 °10′ 07″E, 39 °48′ 34″ ~ 40 ° 10′ 37″N. The mountain area accounts for about 98.5% of the whole area, and the plain accounts for 1.5%. The study area belongs to the middle latitude continental monsoon climate. It’s droughty and windy in spring, hot and rainy in summer, cool and moist in fall, cold and dry in winter. There is great difference between western mountainous area and eastern plain in climate, the annual mean temperature is 11.7 in the east and 10.2 in the west. There are three rivers flowing through the study area— the Yongding River, Daqing River and Beiyun River. Yongding River covers the largest drainage area, which is about 1,368.03 square kilometers. Mentougou integrated ecosystem is composed of mountains, green fields and water system, which is an important ecological barrier and water source conservation area for Beijing. Therefore Mentougou is the key district to ensure the sustainable development of the Capital.

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3 Structure and Function of the System According to the requirement of the eco-conservation and terrain features, the study established the information demonstration area in order to improve the competitiveness of agricultural products, optimize and upgrade the rural economic structure, and transform the pattern of economic growth. The construction of information demonstration area made full use of the advantages of GIS in data management, information visualization and spatial information analysis, integrated the information of natural ecology, agricultural production, special products trading and government, and established an eco-agriculture information service platform. Two towns, Wangping and Miaofenshan, are firstly selected as the experimental units. Then the practice is extended to the whole district. 3.1 Natural Ecological Information Module This module provides information display and query of meteorological, natural vegetation, hydrology, geology, soil type and so on. The meteorological information mainly involves temperature, relative humidity, precipitation and sunshine hours.

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3.2 Agricultural Production Information Module This module mainly records the information of agricultural resources to realize the network management of agricultural production process. The information includes farm fields, soil nutrient, soil fertility, varieties of agricultural products, planting area, tree age and its spatial distribution. The soil nutrient includes the organic matter, total nitrogen, available nitrogen, available phosphorus, available potassium and PH value. In addition, according to standardized production practice, this module records the information of water and fertilizer management, training and pruning , pest control, growth process of crop and so on. All of the above mentioned information can be displayed and queried with the field parcel as a unit. 3.3 Special Products Trading Module Mentougou district produces abundantly special products, such as pear, walnut, cherry, almond and apricot. Some products, named as the tributes to the imperial palace, are famous over China, especially the roses in Xiangjiangou Village, Miaofengshan town enjoy sound reputation both in domestic and overseas due to big flowers, thick petal, dense color, fragrant gout and high oil content. Special products trading module realizes online trading and product tracing. Online trading mainly includes product information publication and recommendation, agricultural product credit guarantees, payment function, distribution process and information flow records. For the function of tracing back the product information, the product identification codes are affixed to the smallest package. With the unique identification code, the platform can query the detailed records of production management, products processing, logistics distribution information, and builds the quality assurance system from the origin of product to the market. 3.4 Government Affair Management Module This module issues the related policies and regulations information, and manages the daily business of towns and villages. The module emphasizes on the statistics analysis of social and economic data to provide the basic economic evaluation. The data includes numbers of households, total population, per capital annual net income, per capital disposable income, annual wages income, household business income, annual property income, annual transfer income, total expenditure and others.

4 Implementing Scheme As shown in Fig.1, implementing scheme of the system is composed of data acquisition and processing, database construction and network platform construction.

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data acquisition

remote sensing data

GPS data

fundamental geographic data

field survey data

statistical data

database construction

platform frame

functional division of the platform

agricultural production information module

natural ecological information module

special products trading module

government affair management module

Mentougou Eco-Agriculture information service platform Fig. 1. Implementing scheme of the system

4.1 Data Acquisition and Processing The basic data and maps need to be collected and integrated. The data includes satellite image map, basic map and thematic map, statistical data on rural economy over years, and other agricultural data of meteorology and envirnment and etc. 4.2 Database Construction Rural information resources mainly include natural resource information, ecological information, agricultural production management information, agricultural market information and government affair information. All the information are processed, formatted and saved in the information resource database, which can be easily saved, retrieved, transmitted, published and shared through modern information technology.


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According to the local conditions, the information demonstration area constructs the spatial and attribute database, among which spatial information service is the basic database. The accuracy, capacity, coverage and update of spatial database not only affect the current construction of information demonstration area, but also have far-reaching influence on the economy of the district and the scientific value of the study. Spatial database integrates the information of topographic, traffic network, administrative division, soil type, soil nutrient, land use status, field parcel distribution, temperature, rainfall and so on. Attribute database includes agricultural production information, product trading information, product tracing and management of administrative villages. All the information is connected with field parcel data. 4.3 Network Platform Construction The study establishes Mentougou Eco-Agriculture information service platform, using .NET, SQL Server and SuperMap as development tools, C# as development language. The platform integrates four above-mentioned information management modules to realize the information query, analysis, decision-making, trading, trace back and others. The browser/server structure is applied to the system, and the client users can access the platform using a web browser.

5 Function Actualization of System This paper takes management of space data and visualization showing for example to show the function actualization of system. Visualization showing interface of Mentougou geographical space data is showed as Fig.2, which realizes management and visualization of data such as village boundary, road, water system, soil, vegetation, landform, farmland and soil using style, etc. The system can brings plane map as well as three-dimensional relief map, both of the ways of showing can realize the zooming in and zooming out of the map. In addition, relief map has flight function, which

Fig. 2. Visualization showing interface of Mentougou geographical space data

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means the user can get access to related information of three-dimensional landform by user-defined route. The system provides two query methods: query map by attribute and query attribute by map. As shown in Fig.3, based on the year, names of town and administrative village selected by the user, the system shows relevant positional information. When the user clicks on the map, the system will show information from the attribute database, such as area, numbers of households, agricultural population, income, climate, fertility of soil, etc.

Fig. 3. Query result of administrative village

6 Conclusion and Prospect The study establishes the information demonstration area based on the technology of .NET, 3S and database to realize the integration of rural information resources. Through fully utilizing the advantages of GIS in map expression, spatial data management and map processing, the information service platform improves the visualization and convenient of management of rural information, advances the management efficiency of rural information. Further study and discussion: (1) To provide technical support for the planting structure adjustment, product variety update and agricultural division based on further study on the database of climate and agricultural resources. (2) To establish the fertilizer recommendation system for green food according to the soil nutrient database of special products, and to provide scientific support for cultivation of high yield and quality with the study of the relation between soil nutrition status and product yield and quantity. (3) To establish the remote sensing monitoring system to monitor diseases and insect pests of special products and provide real reference for decision-making. (4) To build comprehensive evaluation model for ecological capacity maximizing the ecological, economic and social benefits in order to promote the virtuous circle of ecosystem, and provide decision support for sustainable development of the area.


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Acknowledgments This study was funded by the Commission of Science and Technology of Mentougou district, and the project number is D0804090041000.

References He, L.Y., Huang, W., Guo, Z.H., Miao, J.: Status, Task and Problem of Information Demonstration Village Construction in China. In: CCTA 2007, pp. 409–415 (2007) (in Chinese) Li, M.: Demand Analysis and Development Strategy for Informatization of Rural Area in Beijing city. J. Agriculture Network Information, 47–49 (2009) (in Chinese) Shi, Y.Q.: Reflections and Suggestions On Rural Informatization Construction in the Mountain Areas of Beijing. J. Agriculture Network Information, 41–44 (2009) (in Chinese) Zhu, H.J., Wu, H.R., Feng, C., Zhong, X., Sun, X.: The Application of GIS in the Information Service Platform for New Village Construction. J. Journal of Agricultural Mechanization Research, 164–166 (2008) (in Chinese)

Data Acquisition Method for Measuring Mycelium Growth of Microorganism with GIS∗ Juan Yang1,∗∗, Jingyin Zhao1, Qian Guo2, Yunsheng Wang1, and Ruijuan Wang2 1

Technology & Engineering Research Center for Digital Agriculture, Shanghai Academy of Agricultural Sciences, Shanghai 201106, P.R. China 2 Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai 201106, P.R. China [email protected]

Abstract. Mycelium is the vegetative part of a fungus or most microorganisms, consisting of a mass of branching, thread-like hyphae. It is through the hyphae that a fungus absorbs nutrients from its environment. For most fungi, the ability of nutrition translation from mycelium to fruit body is determined by growth status of hyphae. It is very necessary to study the effect of environmental factors on mycelium growth, and know the befitting environment condition. However, finding a good data acquisition method for measuring the mycelium is the key point. A new method was introduced in the paper. The method is using image identification and space data analysis function of the GIS to acquire development rate of mycelium i.e. hyphae. Pleurotus eryngii under commercial production is taken as example. The effect of different temperature and humidity on mycelium growth was analyzed. It is hoped to explore a new method for scientific and precise measurement the growth status and development rate of mycelium. Keywords: mycelium, data acquisition, microorganism, Pleurotus eryngii.

1 Introduction Mycelium is the vegetative part of a fungus or most microorganisms, consisting of a mass of branching, thread-like hyphae. It is through the hyphae that a fungus absorbs nutrients from its environment. Hyphae are very wispy and only several microns long. The structure of hyphae only can be observed by microscope, so the growth of hyphae is usually expressed by morphologic change of mycelium. There are two methods to measure mycelium growth at present. The first method is physical method. By checking the space change of the mark on the forepart of a mycelium or the diameter change of a mycelium in certain period of time, the growth of the mycelia can be observed. However, this method has a big error, moreover, it only adapts to the smooth ∗

The research was supported by the project of the Science and Technology Commission of Shanghai Municipality, China (grant No. 08DZ2210600 and No. 08QA14058) and the National Natural Science Foundation of China (grant No. 30800765). ∗∗ Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 374–380, 2011. © IFIP International Federation for Information Processing 2011

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agar substrate in laboratory [1] and can not be used in nature or in production of edible fungi. For the circumstances outside the laboratory, the substrate is often rough surfaced soil or the admixture of wood chip, corncob chip and so on. Another approach is to measure the fungal-specific biochemical markers [2], which is classified as chemical method. The signature fatty acid 18:2ω6,9 [3], ergostrrolp[4] and chitin[5] have been used as a marker for ectomycorrhizal(EM) fungi[7-8], and the neutral lipid fatty acid 16:1ω5 has been used as a marker for arbuscular mycorrhizal(AM) fungi[8]. For example, ergosterol is a fungus specific lipid used as a marker for living fungal biomass [4,6], by quantifying its ergosterol content where the activity of mycelium was determined[9]. There is another situation for some fungi that the target production is the antiviral, antibacterial or antifungal substances from its secondary metabolites, such as Pycnoporus sanguineus, which produces an important secondary metabolite, cinnabarin. The growth of the fungus was represented by the cinnabarin production [10]. However, most fungi or microorganisms do not have the specific biochemical matters in them. How to quantitatively express the growth of mycelium i.e. hyphae? A new method named photogrammetry was introduced in the paper for measuring the growth of mycelium. The approach was also applied in the GIS data acquisition [11]. The key of the approach is using image identification and space data analysis function of the GIS software. Then the mycelium of Pleurotus eryngii under commercial production was taken as example. The effect of different temperature and humidity on mycelium growth was analyzed. The aim of this paper is to explore a new method for scientific and precise measurement of growth status and development rate of mycelium.

2 Materials and Methods Mycelium Living in All Kinds of Substrate The method is not only appropriate for measuring the growth of mycelium living in the smooth agar substrate in laboratory experiments, but also for the mycelium living in almost all kinds of substrate, for example, the mycelium of Agaricus bisporus(Lange) Sing. living in the soil, the mycelium of Pleurotus eryngii living in the admixture of wood chip, corncob chip and so on. It needs to mention that the method is not applicable for EM fungi or AM fungi, in which the mycelium accretes with plant roots and forms a symbiont. There are no methods having been available to distinguish mycelia from EM fungi or AM fungi from saprotrophic fungal mycelia in soil [7]. Therefore, the amount of EM fungi or AM fungi is usually calculated by the chemical method, which is reflected by their specific compounds. Image Acquisition The strongpoint of this method is that it is no need to destroy the growth of mycelium or touch the mycelium in data acquisiton, so the process and trends of growth of hyphae can be monitored.

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×

The image of mycelium can be obtained by digital cameras with at least 1024 768 pixels or 300 resolutions. Image Processing The obtained photo is usually a color image, which has three bands, respectively, red (band_1), green (band_2) and blue (band_3). Through opening the band image of the color image in the ArcMap software, the monochrome image of the color image can be obtained. In the monochrome image, each pixel has a gray value (usually between 0 and 255) that specifies a particular shade of gray. Black is 0 and white is 255. Taking the mycelium of Pleurotus eryngii as example, the hyphae live in the substrate loaded in the bottle, and the mycelium revealed on the bottle mouth is a window that reflects the growth of the hyphae. The picture of the mycelium on the bottle mouth was taken and opened in the ArcMap software. In the band_3 (blue) image of the three band image, the contrast between mycelium and substrate is the biggest one(see Fig.1). The second step is extracting the region to be analyzed on the photo by the Spatial Analyst Tools——Extraction of the ArcGIS. That is, the useless region is removed (see Fig.2B) and the Object Image Layer is obtainded.

Fig. 1. Three band image of the acquired color image

Fig. 2. The key image layer in the method A.the original image; B. the image that useless region was removed; C. the raster layer of substrate and mycelium pixel.

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Fig. 3. The raster count of the two pixel value in the Attribute Table of the ArcGIS

Data Acquisition With the identification tool in the ArcGIS, the gray value of each pixel can be identified. Because the color of Pleurotus eryngii mycelium is white and the color of substrate is much darker, the gray value tends to 0 for the pixel of Pleurotus eryngii mycelium and to 255 for the pixel of the substrate. It is important to confirm the critical gray value between the mycelium pixel and the substrate pixel. With the critical gray value, a new raster layer can be got by using the Raster Calculator tool of the Spatial Analyst Tools in the ArcGIS (see Fig.2C). In the new raster layer, the pixel of mycelium is valued 1 and the pixel of substrate is valued 0 if input formula in the Raster Calculator tool shows “ ‘the Object Image Layer’ > ‘the critical gray value’ ”, or the pixel of mycelium is valued 0 and the pixel of substrate is valued 1 if input formula in the Raster Calculator tool shows “ ‘the Object Image Layer’ < ‘the critical gray value’ ”. Opening the Attribute Table of the raster layer, the raster count of two pixel values is displaying (see Fig.3). The proportion of mycelium can be calculated through the raster count of mycelium divided by the sum of the raster count of mycelium and substrate. By monitoring the development of the proportion of mycelium in unit times, the development rate of hyphae can be expressed.

3 Application and Discussion Background An example about the effect analysis of temperature and humidity for mycelium growth is given to illustrate the application of the method. There are two phases for the hyphae of Pleurotus eryngii under commercial production. In the first phase, the strains of Pleurotus eryngii are inoculated into the culture medium loaded in plastic bottle. Then the bottle with the strains is incubated under conditions at 25 , 70-75% RH about 25 days, and it is still kept in this situation about 10 days for afterripening after the hyphae spreading into the entire bottle. On top of the bottle, lid is removed and the surface of the culture medium is mechanically scratched to remove the exterior aerial mycelium and a 15mm layer of substrate. This process is used to induce uniform formation of primordia with synchronous mushroom production [12]. The opened bottles are placed in a production room

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controlled at temperature about 18 , 85-95% RH. The hyphae are entering into the second development phase. The second phase is very important, especially the hyphae in the surface of the culture medium are important because they will kink into the bud of mushroom. The air climate control systems for the growing rooms are designed and manufactured by Patron AEM in Netherlands in this study. These unique systems are able to control temperature, humidity and CO2 concentration in growing rooms very precisely and efficiently. In the experiment of the example in this study, treatments included temperature at 14 , 15 , 16 , 17 and 18 with 97% RH, and relative humidity at 89%, 91%, 93%, 95% and 97% at 16 . Three replicates for each treatment were set. The experiment was a 2 (supplement) × 5 (treatment) design with 3 replicates per treatment. The mycelium growth was measured daily (every 24 hours) at 8 bottles for each experiment. Each value is the mean of 24 measured results (3 replicates×8 bottles).

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Results and Analysis In the mycelium growth stage, the mycelium proportion showed remarkable change in different days (Tab.1). The mycelium growth followed the theoretical logistic growth curve exactly (Fig.4). Table 1. Pleurotus eryngii mycelium growth at different times

time/d 1 2 3 4 5 6 mycelium 18.13% a 27.70% b 44.47% c 56.49% d 63.79% e 67.29% f proportion (%) 1 The mycelium proportion was each day’s means of two treatments. Different letters indicate statistically different values (ANOVA/LSD) (P2cm between elevations and their average values. It was clear that the percentage accounted in fast speed was bigger than in the slow speed. This showed that the measurement result got from fast speed had much more discrete data and the accuracy in slow speed was greater than fast speed.

Fig. 8. Statistical chart of percentages of the absolute difference >2cm between elevations and their average values under two moving speeds

5 Conclusion This paper presented a new type of onboard field 3D topography surveying system. The experiment result indicated that the influence of the tractor’s speed to the measurement accuracy was obvious and the measurement accuracy in slow speed was greater than in the fast speed. Different speed should be adopted according to the field condition when doing measurement with onboard field 3D topography surveying system. Slow and uniform speed was important to guarantee the measurement accuracy.

Acknowledgement This research is sponsored by the project 2008BAB38B06 and 2009BAC55B01. All of the mentioned support is gratefully acknowledged.

References 1. 2.

Rickman, J.F.: Manual for laser land leveling, pp. 1–5. Indian Council of Agricultural Research, New Delhi (2002) Li, Y., Xu, D., Li, F.: Application of GPS Technology in Agricultural Land Leveling Survey. Transaction of the CSAE 21(1), 66–70 (2005)

416 3. 4. 5. 6. 7. 8.

M. Guo, G. Liu, and X. Li Chen, Y.: Research and Development on Field Topography Measurement Equipment based on GPS and Laser Techniques. China Agriculture University, Beijing (2006) Zhang, M., Chen, Y., Jia, W.: Design of 3D Topographic Information Measuring System. Journal of Jilin University: Engineering and Technology Edition 37(6), 1451–1454 (2007) Yang, Z.: Research and Development on Field Topography Survey System based on GPS and Laser Techniques. China Agriculture University, Beijing (2008) Lv, Q.: Improvement and Experimentation of Laser Controlled Land Leveling System. China Agriculture University, Beijing (2007) Lin, J.: Research and Development on Receiver and Controller for Laser Controlled Land Leveling System. China Agriculture University, Beijing (2004) Si, Y., Liu, G., Yang, Z.: Development and Experiment on laser land Leveling System. Journal of Jiangsu University: Natural Science Edition 30(4), 69–74 (2009)

Implementation of Agro-environmental Information Service System Based on WebGIS Lin Peng1,2, Linnan Yang2,*, and Limin Zhang2 1

College of Computer Engineering and Science, Shanghai University, 200072 Shanghai, P.R. China 2 College of Basic Science & Information Engineering, Yunnan Agricultural University, 650201 Kunmin, P.R. China [email protected], [email protected], [email protected]

Abstract. Faced to the present agro-environmental information features, there exist several difficulties to acquire and control the agricultural environment information, such as the scattered information with spatio-tempel traits, the methods of quantification and the huge data amount. This paper constructed an agro-environmental information service system based on the spatial database, computer network and geographic information system (GIS) technology. This system was applied in Jianshui County, Yunnan to implement the system functions including the collection, storage, analysis, visual output and intelligent evaluation. The system with these functions applied technical support for Jianshui county to improve the abilities both in local agricultural products and environmental protection. And it provided a precedent for other Counties in Yunnan to construct agricultural environmental information system. Keywords: WebGIS, Agricultural environment, Information service system, Spatial database.

1 Introduction Both of the qualities and the quantities of agricultural products are the most important aspects for farmers, agricultural technicians and managements. In modern agricultural processes, how to improve the products’ qualities is much more increasingly come into people’s attention than to improve the quantities. And the qualities of the agricultural products include many strict standards such as pollution-free food standards and green food standards etc. In order to meet these standards, the basic step is to monitor and protect agricultural ecology environment because there is impossible to gain any high quality agricultural products from heavy polluted air, soil and water. Any environment protection measure would be blind if there is no monitor to gain plenty of quantification environment information. Only though the environment monitoring to acquire appropriate environment information data could understand the reasons why the pollutions created and the regularities that the pollutions changed. Then these reasons and regularities are significant for agricultural D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 417–427, 2011. © IFIP International Federation for Information Processing 2011

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technicians and managements to formulate practical environment protection plans. Thus, contemporary computer technologies were be used frequently to obtain temporal and spatial ago-environmental information in different scales. But since agricultural environment information is scattered and indirect, it is difficult to use the obtained information data fully to promote environment protecting level [1]. And all of these problems are particularly realistic in Yunnan Province, which is located in the southwest of China. The land in Yunnan is varied from mountain on Yungui Plateau at the altitude of 6740 meters to valley at the altitude of 76 meters. The prominent disparity of altitude arouses multiple land forms and complex climates. This unique geographical environment imposed diversification on Yunnan agricultural structure, and increased the difficulties in environment monitoring for agricultural workers, technicians and managements. This paper aimed at obtained environment monitoring data from 2005 to 2009 in Jianshui County, Yunnan designed and implemented a new agro-environmental information service system based on WebGIS. Firstly, the relative agro-environmental information data were aggregated and classified such as atmosphere information, soil information, heavy metal information in soil, irrigation water information. Secondly, the agro-environmental information service system was designed based on the technologies including computer, spatial database, network, and geographical information system (GIS). Compared with the used information service system this new system implemented inquiry, modification, addition and deletion etc. operations on mass environmental information data, and applied the local farmers directly, visually and comprehensively agro-environmental information service. The new system integrated agro-environmental information data and corresponding evaluation model to implement intelligent evaluations on the environmental pollution levels including air, water, soil and soil fertility.

2 System Design 2.1 System Development Methodology First, attribute database was built based on collected data such as environmental quality standards, atmosphere data, fertility data of soil, heavy metal data of soil, irrigation water data and so on according to the investigation in Jianshui. Second, spatial database was built according to the administrative map of Jianshui and some spatial data such as land form data, monitoring spot data, pollution elements data in soil and so on. And profile the corresponding digital map of these spatial data at the same time. Third, a multi-index evaluation model was developed based on the above attribute database and spatial database to analyze the contamination degree of the air, water, soil and the soil fertility level. The evaluation model was integrated into WebGIS Components and could be used by consumers. The technique flow diagram of the system development was illustrated in Fig.1.

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Fig. 1. Technique flow diagram of the system development

2.2 System Development Platform The agro-environmental information service system is built on the network technical architecture, so this paper integrated the network and GIS technology to develop and implement a practical agro-environmental information service system in Jianshui, Yunnan. SuperMap IS.NET was chosen to be the development platform in the new information service system, because the function and structure of SuperMap IS.NET WebGIS platform could satisfy the system development requirements which should be completed with compatibility, expansion, generalized data exchange format. And SuperMap Deskpro5 was chosen to be the plotting software to draw electronic map to improve the equipment compatibility because both of the SuperMap IS.NET and SuperMap Deskpro5 are the products from the same software company. At the same time, since the system would face the challenge of mass data storages and operations, SQL Server 2005 was used as system database development tool to solve the expansion and integration difficulties of the system database.

3 The Implementation of the System 3.1 System Structure Architecture. B/S/D (Browser/Service/Database) three-layer architecture was used to implement the design and development of the system expandable, make the system module components reusable, and make the system services independent.

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Fig. 2. Architecture diagram of the system

(1) Database Layer SQL server database software was used as supporting software in database layer. All the designs included five spatial databases. They were spatial database of production resources information, spatial database of soil fertility information, spatial database of soil pollution information, spatial database of irrigation water information and spatial database of climate information spatial database. (2) Service Layer SuperMap IS 2008 was used in Service layer to support the map generation and management functions. (3) Browser Layer IE6.0 or above version was adopted to be the browser to develop the website. Web controls were used to invoke both of the services in the Service Layer and the User Interface. Browser layer has the functions of browsing map, editing map, spatial Analysis, query statistics, etc. Network Structural. Network structural designing mainly resolved two problems: (1) How to access different server (2) How to meet the requirements of different access pressure.

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Fig. 3. Network structure diagram of the system

3.2 The Data Arrangement Data analysis. There were two types of data in this system, vector data and raster data. The data design and the analysis were showed in Fig. 4.

Fig. 4. Data analysis diagram of the system

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With the assistance of Jianshui Agricultural Bureau, chose 10 villages in 3 towns, they are Lin’an, Miandian and Puxiong in Jianshui, to be the investigation sites. Then, the agro-environmental information, which included atmosphere data, irrigation water data and soil data in these 10 villages were collected according to national environmental requirements on agricultural products base. Among these national standard, this system adopted GB5084-92 national standard on farm irrigation water, GB 3095-1996 national standard on atmosphere, and GB15618-1995 national standard on soil. The collected data mainly includes geospatial information data and attribute data. (1) Geospatial information data The geospatial information data is stored in ‘eoo’ format based on the administrative region map in Jianshui at 1: 50000. (2) Attribute data The attribute data included main environmental information from 2005 to 2009, such as air data, irrigation water data, soil data, planted area data and other basic data. These data were provided by the Jianshui Agricultural Bureau from Status Survey on Agro- environmental in JianShui. The soil database sample was showed in table 1.

Table 1. Air pollution Index data

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In the formula (3), a-centre distance of the angular displacement sensor and the seed tape disk, m. l-length of the radius detecting pendulum, m. r-radius of the seed tape disk radius detecting roller, m. It uses 57BYGH306 step motor and DL-023MDC step motor driver to achieve the rotating speed adjustment of the seed tape disk. The rotating speed of the step motor depends on the pulse frequency, step angle depends on the microstep of the step motor driver. When the step motor driver in the input pulse of 200Hz, it is in the concussion zone, easy to damage the internal components. So apply pulse of 350Hz as the low frequency starting point. In order to satisfy the step motor driver input pulse, it set transmission ratio of 4:1 (that is n2 / n1 = 4 / 1 ) gear-driven to the seed tape disk. It has, n1 =

60 ⋅ θ ⋅ f 360o

(4)

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In the formula (4), θ -step angle of step motor, º. f-pulse frequency of step motor driver, HZ. n1 rotating speed of step motor, r/min.

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2.3 Working Principle of the Automatic Control System for Linear Velocity of the Seed Tape

Working principle of the automatic control system for linear velocity of the seed tape is shown in Figure 3. It is a control system of the constant value. Initial value of the linear velocity (v0) is set from the keyboard of the MCU. Through the MCU operating, it drives the step motor work in the rotating speed of n0 and produces an initial voltage value (V). Compare V with the Vt , which is converted from the feedback device of the seed tape radius detection, and get V. After AD conversion, amplification and calculation of the MCU, it outputs frequency (f) to step motor driver to change the rotating speed of the step motor, adjusts the rotating speed of the seed tape disk, outputs actual linear velocity (vm) of the seed tape. The radius of the seed tape disk (R) increased, when the numbers of the seed tape winding on the disk increasing. The value of R is detected by the feedback device of the seed tape radius detection to output voltage value (Vt) and compared with initial voltage value to complete the process of the automatic control system. The automatic control system communicates with host computer through the RS232 bus to realize data acquisition, storage, and comparison analysis and so on.

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3 Results and Discussion The prototype test of the hardware system has been done to verify the accuracy of the automatic control system. Contrast curves of the theoretical rotating speed and actual rotating speed of the seed tape disk are shown in Figure 4. It can be seen from Figure 4 that the rotating speed of the seed tape disk present ladder form decreases with the time increasing during the winding process. The reason is that the rotating speed of the seed tape disk decreases with the seed tape disk radius increasing, while the radius of the seed tape disk increases only after the seed tape winding fully one layer on the disk, which changes the equivalent to a step signal. The time of one layer spending is different in a constant linear velocity, so the changes of the rotating speed of the seed tape disk and the time into the relationship of ladders.

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rotating speed of the seed tape disc shaft /r/min

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Descriptive statistics of the actual seed rope linear velocity has been listed in table 1. After comparative analysis with the theoretical linear velocity (0.1m/s), the changes of the actual linear velocity is little, it meets the requirements of design. Table 1. Descriptive statistics of the actual seed rope linear velocity Linear velocity mean /m 0.101

Std. deviation /m 0.0047

Minimum

Maximum

/m 0.092

/m 0.109

Coefficient of variation /% 4.64

4 Conclusions (1) It has designed rice seed tape twisting machine in this paper. The machine consists of non-woven feeding mechanism, binder brushing mechanism, seed sowing mechanism, fertilizer sowing mechanism, materials locating mechanism, twisting mechanism, drying mechanism, seed tape disk winding mechanism, seed tape fracture testing mechanism and so on. The machine can drop seeds and fertilizers on the spunbonded non-woven fabric made of polylactide (PLA) and made into seed tape disk. (2) It has designed a two-way spiral camshaft driven by the seed tape disk shaft to realize seed tape reciprocating winding in the disk, when the diameters of the disk bigger, the rotating of the spiral camshaft slower, which ensures the seed tape disk uniformity of winding. That realizes the constant speed of the seed tape. (3) It has studied an automatic control system for linear velocity of the seed tape in this paper. The system consists of seed tape disk radius detecting roller, seed tape disk radius detecting pendulum, angular displacement sensor, MCU, step motor driver and the seed tape disk. It overcomes the shortcomings of the first-generation prototype such as the seed tape breaking easily because the diameters of the disk increase continuously and the tensions on the seed tape are bigger and bigger. The experimental results show that the machine performs well. The actual rotating speed slowed in

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ladder figure when the diameters of the disk increased. Analysis by the SPSS, the linear velocity mean was 0.101m/s, the Std. deviation was 0.0047, the coefficient of variation of the actual seed rope linear velocity was 4.64%. It has no fracture during the process of twisting working, it meets the design requirements. The system also has advantages of low cost and suitable for promotion and application.

References 1. Lv, X.R., Ren, W.T.: Analysis and Research for the Technology of Rice Direct Sowing with Seed Rope. Journal of Agricultural Mechanization Research 1, 212–215 (2008) (in Chinese) 2. Ren, W.T., Li, X.S., Cui, H.G.: Effect of the Technigue of Rice Direct Sowing with Seeds Twisted in Paper Rope on Rice Yield Character. Journal of Shenyang Agricultural University 36(3), 265–270 (2005) (in Chinese) 3. Ren, W.T., Li, X.S., Zhang, Y.S.: Development and Experiment on a Rice Seed Rope Twisting Machine. Journal of Agricultural Mechanization Research 6, 169–172 (2005) (in Chinese) 4. Zhou, J., Ji, C.Y.: Development of Machine for Producing Precision Seeding Rope with Paper For Rice Direct Sowing. Transaction of the CSAE 25(7), 79–83 (2009) (in Chinese) 5. Zhou, J., Ji, C.Y.: Machine for Producing Rice Seed Rope and Field Experiment. Journal of China Agricultural University 14(2), 98–102 (2009) (in Chinese) 6. Zhang, T., Ren, W.T., Ma, Y.: Parameter Analysis on Twisting Rope Machine of Rice Seed Rope Twisting Machine. Journal of Agricultural Mechanization Research 12, 78–79 (2006) (in Chinese) 7. Ren, W.T., Lv, X.R., Zhang, B.H.: Dynamic Response of Taped Type Rice Direct Seeding Machine for Field Surface Roughness. Transactions of the Chinese Society for Agricultural Machinery 40(8), 58–61 (2009) (in Chinese) 8. Ren, W.T., Dong, B., Cui, H.G.: Experiment on the Motion Characteristics of Rice Seeds after Collision with Different Slopes. Transactions of the CSAE 25(7), 103–107 (2009) (in Chinese) 9. Ren, W.T., Yang, Y., Zhang, B.H.: Design and Implementation of Automatic Control System for Sectional Type Subsurface Drip Irrigation in Greenhouse. Transactions of the CSAE 25(8), 59–63 (2009) 10. Ren, W.T., Dai, L.L., Cui, H.G.: Effect of Modified Maize Starch Binder on the Quality of Seed Tape Twisting. Transactions of the CSAE 26(5), 164–169 (2010) (in Chinese) 11. Luo, X.W., Liu, T., Jiang, E.C.: Design and Experiment of Hill Sowing Wheel of Precision Rice Direct-seeder. Transactions of the CSAE 23(3), 108–112 (2007) (in Chinese) 12. He, R.Y., Luo, H.Y., Li, Y.T.: Comparison and Analysis of Different Rice Planting Methods in China. Transactions of the CSAE 24(1), 167–171 (2008) (in Chinese) 13. Guo, Y.X., Liu, W.N., Zhao, Q.X.: Study on Microprocess Control System Based on Precise Sowing. Journal of Agricultural Mechanization Research 9, 81–83 (2008) (in Chinese) 14. Qiao, X.J., Shen, Z.R., Chen, Q.Y.: Design and Realization of General Computer Monitoring and Controlling System for Environment of Agricultural Facilities. Transactions of the CSAE 16(3), 77–80 (2000) (in Chinese) 15. Wu, Q.H., Xu, B.Q.: Analysis of Synchronized Control System for Multi-Motor. Autocontrol O.I.Automation 22(1), 20–24 (2003)

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16. Kevin, P.: Synchronized Motion Control withthe Virtual Shaft Control Algorithm and Acceleration Feedback. In: Proceedings of the American Control Conference, American, pp. 2102–2106 (1999) 17. Anderson, R.G., Meyer, A.J., Valenzhuela, M.A.: Web Machine Coordinated Motion Control Viaelectronic line-Shafting. IEEE.Trans. Ind. Application 37(1), 247–254 (2001) 18. Xu, H.B., Du, X.B.: Control System Design for Stepper Motors. Journal of Chongqing Institute of Technology (Natural Science) 22(5), 32–34 (2008) 19. Zhong, Y.S., Yang, J.Q., Deng, J.L.: Multivariable Fuzzy Control of Temperature and Humidity in a Greenhouse. Transaction of the Chinese Society for Agricultural Machinery 32(3), 75–78 (2001) (in Chinese) 20. Gong, C.K., Chen, C.Y., Mao, H.P.: Multivariable Fuzzy Control and Simulation of a Greenhouse Environment. Transaction of the Chinese Society for Agricultural Machinery 31(6), 52–54 (2000) (in Chinese) 21. Ding, W.M., Wang, X.C., Li, Y.N.: Review on Environmental Control and Simulation Models for Greenhouse. Transaction of the Chinese Society for Agricultural Machinery 40(5), 162–168 (2009) (in Chinese) 22. Yu, Y.C., Hu, J.D., Mao, P.J.: Fuzzy Control for Environment Parameters in Greenhouse. Transaction of the Chinese Society of Agricultural Engineering 18(2), 72–75 (2002) (in Chinese)

Design and Implementation of Crop Potential Model System Based on GIS and Componentware Technology Hao Zhang1, Li Ding1, Guang Zheng1, Xin Xu1, Lei Xi1,*, and Xinming Ma1,2 1

College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China 2 College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China [email protected], [email protected]

Abstract. Based on SuperMap IS.NET and the empirical models about crop potential output, the paper firstly designed the model system of crop potential output by using componentware method based on distributed computing architecture under network environment. Secondly, the paper implemented crop potential model components by using componentware technology. Finally, with the abstract mechanism of interface, the paper integrated crop potential output models and loosely coupled model components with SuperMap GIS. The results show that the model system as a component container about crop potential output model integrated empirical and mechanism models and provided a dynamic management for crop potential output models and dynamic methods call, which solved the issues of integration and expansion, and the system has the characteristics of wide applicability and good independence, which provides ADM and technical support for the construction of major grain-producing areas, crop production management and potential mining. Keywords: Componentware, Crop potential output, GIS, UML.

1 Introduction Currently, digital model on crop production system is the foundation and the core of the digital agriculture, and is also the bridge linking the planting digitization, intelligentization and precision[1-4]. The significance of crop potential output model lies in the quantitative analysis and evaluation for the factors’ role to the whole crop producing stage, and explains the factors’ impact to crop potential output in detail. Crop potential model has advantages of strong explanatory power, wide application and easy to quantify and be controlled. With the development of crop potential output evaluation model, componentware and GIS, GIS-driven software development methods and componentware technology have been widely used in various types of GIS application system. It is effective to reduce the coupling GIS with model system and improve the maintainability and independence by using componentware technology[5-6]. In this case, the paper adopted the *



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componentware method to build a strong extension and low coupling crop potential model system based on SuperMap IS.NET 2008, accomplished crop production information management and potential analysis, and provided technical support and decision-making for managing crop production and mining crop potential.

2 Data Source and Research Method 2.1 Data Source Research data includes meteorological data, soil data, socio-economic data and crop production data. Meteorological data includes daily average temperature, daily maximum temperature, daily minimum temperature, rainfall and sunshine hours, provided by the weather bureau of Henan Province. Soil information includes soil texture, soil type and soil nutrients, provided by the county soil station or compiled through Henan TuRang Dili[7]. Socio-economic data includes producing condition, economic condition and producing level. Crop production data includes crop varieties, planting region, annual crop area, yield per hectare, annual total output and multiple crop index, collected from the statistical yearbook of Henan Province. 2.2 Research Method Crop Potential Output Evaluation Process. First, based on the process and the method of crop potential output evaluation[8-9], the paper collected and collated attribute data, such as Meteorology, soil and social production, and spatial data at 30 counties in Henan Province, to integrate space and attributes database. Second, the paper built the model components at all levels based on crop potential output model about light, temperature, water and soil and realized the model system of crop potential output. Finally, the paper quantitatively calculated crop potential output through mechanism model. Crop Potential Output Mechanism Models. The frequent methods of crop potential output included mechanism model and empirical model[10-12] at present. The latter was complicated and had the disadvantage of more inputted parameters leading to be difficult to spread, but the former was simple and easy to popularize. So, the paper used mechanism model of light, temperature, water, soil and social-economic factors, designed crop potential output model components, and constructed a crop production potential model system. Crop potential output model included computing and analysis models of crop potential output. Computing model included natural and social resource calculation. Computing model of natural resource included solar radiation model, photosynthetic potential model, temperature potential model, climate potential model and soil potential model. Computing model of social resource was used to quantify social factors’ contribution, such as producing condition, economic condition and producing level. The daily and total solar radiations were calculated during crop growth according to reference [13]. The potential output of photosynthesis, temperature, climate and soil was calculated according to reference [14-18]. Similarly, Analysis model of crop potential output included natural and social resource analysis, and the analysis approaches included spatial analysis and time analysis.

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3 Design of Model System 3.1 System Architecture Crop potential output model system consisted of system tool, system data, potential calculation and potential evaluation. System tool was responsible for system data management and made up of data editing components and model components for generating meteorological data of previous and current years. System data included the input database and the output database. The input database was made up of regional data, meteorological data, soil data, varieties data and crop cultivation and harvest data, which provided data-driven function for computing model components of potential output. The output database was made up of potential coefficient data, total potential data and thematic evaluation data, which provided data-driven function for model verification and potential distribution evaluation model component. Crop potential output computing component was made up of the standard parameter library of crop species, regional parameter library of crop species, metadata and model file library. Driven by the input database, system generated a set of crop potential output model views. Driven by model management, models were imported and exported, and model parameters were adjusted according to local conditions. Driven by the suitable potential model, crop potential output coefficients and total potential output were calculated and exported into the output database. Driven by crop potential evaluation components, crop potential output thematic analysis and the credibility of potential models were realized. Fig.1. shows the architecture of crop potential output model system.

Fig. 1. System architecture

3.2 Design of Crop Potential Model Component Crop has different growth characteristics and crop potential output was affected by multiple factors. So, the paper used computer technology to calculate crop potential and input all relevant data, such as climate conditions, soil conditions, crop species

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and other data and parameters. Based on the analysis to wheat, maize, cotton and other potential output model, the constructed model component should have the characteristics of abstract and polymorphism, which could cover a variety of crop potential output model. The paper built the corresponding model components by using componentware technology to screen the difference in the calculation process. In crop model components, the sub-model should be refined as far as possible to build an autocephalous atom component. In addition, model components' integration and expansion should be considered with other agricultural production system, so the interface technology was used to highly abstract model components and build a unified data interface. Crop potential output model system was made up of various types of crop model components, such as crop species interface, potential computing interface, and potential evaluation interfaces. Design of Crop Potential Model Interface. According to the empirical models of crop potential output, system model components consisted of the interface and class of photosynthetic potential, temperature potential, climate potential, soil potential, and socio-economic potential. In view of photosynthesis potential associated with solar radiation, solar radiation object was designed as a attribute in photosynthetic potential class, and solar radiation class was also associated with photosynthetic potential class. Crop potential output interface was provided from crop potential output model system, and was responsible for calculating photosynthesis, temperature, climate, soil and socio-economic potential coefficient and total potential. Photosynthesis, temperature, climate, soil and society sub-interfaces and classes were derived from

Fig. 2. Model component interface of crop potential output


Fig. 3. The integrated potential model components

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crop potential output interface. Five classes at all levels of the potential output, potential coefficient and the total amount were derived from the corresponding subinterface. Taking into account the relationship among models and the integration of different models at all levels, interface technology was used to design coupled models loosely in crop potential output model system. Fig.2. shows the model component interface of crop potential output. For example, photosynthetic potential output class was associated with solar radiation interface, and solar radiation class was integrated into the photosynthetic potential class to obtain solar radiation. Solar radiation class realized the interface, regardless of which type of solar radiation class for calculating the total solar radiation, as well as temperature potential class, climate potential class, soil potential class and social potential class. Using interface abstraction mechanism could greatly improve the scalability of crop potential output model system. Design and Integration of Crop Potential Model Subcomponent. Crop potential model subcomponents included photosynthetic potential component, temperature potential component, climate potential component, soil potential and social potential component. Taking into account the characteristics of multi-crop and multi-model, interface technology was used to effectively integrate all levels of models for improving model system’s scalability and maintainability. Fig.3. shows the integrated potential model components.

4 System Implementation and Application 4.1 System Implementation The container component of crop potential model system provided the potential model interface for different crop production management and dynamic function calls, imported all kinds of crop potential models, and retained the further capacity for expansion interfaces to integrate other agriculture information system. Taking wheat and maize production in Henan Province as example, the meteorological data, soil data and crop cultivation and harvest data of all cities in Henan Province were inputted into the model system, the potential yield of wheat and maize was quantitatively estimated through the calculating model subsystem, and the potential distribution of wheat and maize at all cities was qualitatively analyzed through the evaluation model subsystem, which provided the decision-making and technical support for building the core area of crop in Henan Province. 4.2 System Application The system has been applied to crop production management in Henan Province. The system is running well, and effectively estimates and predicts crop potential output. Fig.4. shows the interface of crop potential calculation. Fig.5. shows the interface of crop potential analysis.


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Fig. 4. The interface of crop potential calculation

Fig. 5. The interface of crop potential analysis

5 Conclusion Crop potential output model system was built by using componentware technology based on GIS, and crop potential was calculated and quantitatively analyzed. The results show that the system has the characteristics of wide applicability and good independence, which provides ADM and technical support for construction of major grain-producing areas, crop production management and potential mining. The model system could be extended to other crop potential calculation and also combined with other software system, such as soil productivity evaluation system, agriculture fertilization expert system and crop production early warning system.

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It is necessary to strengthen the function of crop potential model management, improve the component management and enrich the model types of crop potential practically to increase productivity in the model system, such as crop growth simulation models, nutrient dynamics and balanced fertilization models and crop output benefit models. Acknowledgments. This work is supported by "Eleventh Five-Year" national scientific and technological support and major project plan: "High-yield Crop Science and Technology Engineering" (Contract Number: 2006BAD02A07-4), specific industry research sponsored by ministry of agriculture (Contract Number: 20083028) and ”863” plan(Contract Number: 2006AA10Z271). Sincerely thanks are also due to the National Engineering Research Center of Wheat for providing the data for the study.

References 1. Gao, L.Z., Jin, Z.Q., Huang, Y., Chen, H.: The Combination of Crop Simulation and Cultivation Optimization Theory-RCSODS. Crops 3, 4–7 (1994) 2. Zhao, C.J., Wu, H.R., Yang, B.Z., Sun, X., Wang, J.H., Gu, J.Q.: Development Platform for Agricultural Intelligent System Based on Techno-Componentware Model. Trans. CSAE 2, 140–143 (2004) 3. Cao, W.X., Zhu, Y.: Crop Management Knowledge Model. China Agriculture Press, Beijing (2005) 4. Xi, L., Ma, X.M., Li, F.C., Liu, H.B., Ren, Y.N., Li, Y.H.: Design and Implementation of Crop Growth Simulation System Based on Techno-Componentware Model. Jour. Hen. Agri. Uni. 3, 317–320 (2005) 5. Zhang, H., Li, F.C., Ma, X.M., Gao, R., Xia, B., Lu, Z.M.: Design and Realization of GISBased County Testing Soil for Wheat Formulated Fertilization System. Jour. Hen. Agri. Uni. 5, 566–569 (2008) 6. Zhu, Y., Cao, W.X., Wang, S.H., Pan, J.: Application of Soft Component Technology to Design of Intelligent Decision-Making System for Crop Management. Trans. CSAE 1, 132–136 (2003) 7. Wei, K.X.: Henan TuRang Dili. Henan Science and Technology Press, Henan (1995) 8. Zhang, H., Xi, L., Xu, X., Gao, R., Ma, X.M., Yin, J.: Evaluation System of Wheat Natural Potential Productivity at County Scale Based on GIS. Trans. CSAE 12, 198–205 (2009) 9. Zhou, Z.G., Meng, Y.L., Cao, W.X.: Knowledge Model and GIS-based Crop Potential Productivity Evaluation. Scie. Agri. Sini. 6, 1142–1147 (2005) 10. Bai, L.P., Chen, F.: Status and Evaluations on Research of Crop Production Potential in China and Abroad. Crops 1, 7–9 (2002) 11. Xu, C.D., Gao, X.F.: Crop Productivity Potential Model Applied in China. Jour. Arid. Land. Res. Envi. 6, 108–112 (2003) 12. Gu, D.Y., Liu, J.G., Yang, Z.Q., Yin, J.: Reviews on Crop Productivity Potential Researches. Agri. Rese. Arid. Area. 5, 89–94 (2007) 13. Zheng, G.Q.: MAIZESIM——A Model to Simulate Maize Growth and Development. Nanjing Agricultural University, Nanjing (1999) 14. Huang, B.W.: Natural Conditions and Crop Production—Photosynthetic Potential. Science Press, Beijing (1985)


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15. Agricultural Climate Resources in China, http://sky.hzau.edu.cn/BookPath/index.htm 16. Mao, Y.L.: Estimation of Potential Productivities of Main Crops in the Coastal Area of Fujian Province. Jour. Fu. Agri. Fore. Uni.: Natu. Scie. Edit. 2, 255–258 (2002) 17. Shao, X.M., Liu, C.L.: A Study on the Agricultural Potential Land Productivity in the Northwestern Shandong. Chinese Jour. Agro. 4, 5–10 (2004) 18. Wang, H.B., Xu, H., Huo, X.L., Ren, J.Q.: A Study on the Calculating Method of the Soil Effective Coefficient in the Alluvial Plain. Jour. Agri. Uni. Hebei. 4, 53–56 (2002)

Design and Realization of a VRGIS-Based Digital Agricultural Region Management System* Xiaojun Liu, Yuou Zhang, Weixing Cao, and Yan Zhu** Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China Tel.: 025-84396565, Fax: 025-84396672 [email protected], [email protected]

Abstract. In order to realize the digitalization and visualization of information management in agricultural-region, a VRGIS-based digital agricultural region management system (VDARMS) was developed with SuperMap 2008 as the platform of spatial information management, VRMap 3.0 as the driver of scene, and integrating with the existing knowledge model for crop management. This system realized the functions as file management, spatial handling, information query, data analysis, cultural management plan design, virtual simulation, and system maintenance, etc. Case studies of the system were carried out in Heheng village of Jiangyan city and Qinglong village of Nanjing city, Jiangsu province, China, the application results indicated that VDARMS accorded with the development of modern agricultural spatial information management, realized standard, digital management and visual display of agricultural information, and ∗ greatly promoted the development of digital agriculture technology. Keywords: VRGIS, agro-region, knowledge model for crop management, information management, virtual simulation.

1 Introduction Along with the development of information and computer technologies, digital agriculture has been an effective method for many countries to promote the skills of agricultural production and improve the capacity of agricultural competition (Liu, 2005; Song et al., 2007; Zhou, 2009). Agricultural information management system is one of the core technologies in the technological system of digital agriculture. Up to now, correlative researches were reported at home and abroad, for example, Shane et al. (2001) in Kansas State University developed a field-level geographic information system based on object-oriented programming concept. Ma et al. (2007) established a spatial information management system of digital agriculture by using ComGIS *

Project supported by the National High-Technology Research and Development Program of China (No. 2006AA10A303). ** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 446–455, 2011. © IFIP International Federation for Information Processing 2011

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technology. Liu et al. (2006) designed and realized a WebGIS-based system for agricultural spatial information management and aided decision-making with Brower/Server mode as distributing network structure and WebGIS as spatial information management platform. These systems were mostly developed based on GIS, had the functions as information query, spatial analysis and aided decision- making. However, geometrical and topological information in the third dimension (vertical direction) were disappeared for using 2D showing mode in expressing spatial information, so the realistic world cannot be exhibited perfectly. Virtual reality geographic information system (VRGIS) is one of the pop research domains in geographic information system (GIS) and virtual reality (VR). As the integrating combination of GIS and VR, VRGIS has the functions as spatial information management of GIS and virtual visualization of VR, user can observe, immerge and communicate in the virtual environment (Deng et al., 2002). Recently, several correlative researches based on VRGIS were reported in the domains of urban layout, forest resource management and tour resources management, moreover, a series of virtual simulation systems were developed (Jiang et al., 2008; Zou et al., 2006; Liu et al, 2006; Wu et al, 2008). Although these systems realized the functions as 3D wandering, data management and information query, but couldn’t realize the spatial information analysis and decision support. Therefore, the object of this paper is to develop a VRGIS-based digital agricultual region management system by integrating the technologies of VRGIS, crop management knowledge model, database and decision support system (Cao, 2008). The system aims to realize the functions such as digitalization management for agro-region information, aided decision-making for crop cultivation and visualization simulation display, furthermore improve the efficiency and effect of information management and display in agro-region.

2 Design of System 2.1 Design of System Structure Based on the theory of system engineering, software technology, research target and technology characteristic, the system was composed of three parts: user layer, operational logic layer and basal data layer (Fig. 1). 2.1.1 Basal Data Layer The Basal Data Layer Managed Elementary Data and 3D Model Data. Elementary data: included spatial and attributive data. The spatial data included the data as road, watershed, building and field in agro-region. The attributive data was composed of elementary attributive data and geographic attributive data. The first one consisted of agricultural resource data that driving model run and scene constructing parameters, such as meteorological data, soil data, variety parameter data and terrain creating parameters, etc; the second one was the data that described spatial characters, such as position, area, aspiration information of building, crop type, yield and variaty information, etc, these data were connected with spatial data by correlative code.

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User layer Information query Virtual simulation

File management Data analysis Spatial handling

System maintenance Cultural plan design

Operational logic layer GIS platform

Crop management knowledge model

3D virtual platform

Spatial information management

Design for crop management plan

Virtual simulation of agro-region

Basal data layer Elementary data

DEM

DOM

DM

Fig. 1. Framework of system

3D model data: included digital elevation model (DEM), digital orthograph model (DOM) and digital model (DM). DEM was used to simulate the variable status of terrain by digital vector and grid formats (Huang et al., 2001). DOM was the digital image to express the 3D model’s textures (Hu, 2007), DM was the 3D model of ground object with complicated structure by 3D modeling software (such as 3DS Max), moreover it was the foundation of 3D virtual scene. 2.1.2 Operational Logic Layer The operational logic layer consisted of spatial information management module, plan design module for crop cultivation and virtual simulation module of agricultural-region, three modules were integrated based on data level and COM components. The first module could realize information query, data analysis, display of thematic map and maintenance by GIS platform. The second module could realize the decision support for agricultural production management based on existing crop management knowledge model, result data were transferred to GIS platform and 3D virtual platform to fulfill the display of thematic map and virtual scene. The third module could realize the display and alternation of 3D virtual scene, alternation between 2D digital map and 3D scene on the 3D virtual platform. 2.1.3 User Layer Taking Windows as system interface, the system could communicate with user by menu, toolbar, list table, map and 3D virtual scene, etc. By clicking the mouse or keyboard, user could operate the functions such as file management, spatial handling, information query, data analysis, crop cultural plan design, virtual simulation and system maintenance, etc.


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2.2 Design of System Function In order to realize the digitalization and visualization of information management in agricultural region, and supply the convenient alternation, the functions of file management, spatial handling, information query, data analysis, cultural plan design, virtual simulation and system maintenance were designed in this system (Fig. 2).

VRGIS-based digital agricultural region management system

Map management File management Scene management Map handling

Spatial handling

Scene handling

Spatial data measure Buffer analysis

Spatial information query Stack-up

Information query

Attributive information query Statistical analysis

Data analysis

Visual analysis

Cultural plan design

Variety selection Scene automatic creation Alternation between map and scene Scene change

Virtual simulation

Spatial data maintenance System maintenance

Sowing/transplanting Density/basal seedling Fertilizer and water management

Attributive data maintenance Scene parameter maintenance

Fig. 2. Functional chart of system

3 System Realization 3.1 Basal Data Disposal 3.1.1 Construction of Spatial Database The original spatial data of agricultural region were acquired from RS images and GPS coordinates by manual vectorization, disposed data were stored into spatial database by spatial data engine of GIS. SuperMap 2008 software can easily realize the operations as data browse, data edit, information query, result output, spatial analysis and 3D modeling for its good alternating interfaces and convenient handling. Furthermore, all kinds of spatial objects and RS image data can store into database or file by spatial data engine of SuperMap SDX+. Based on all these virtues, we selected SuperMap Deskpro 2008 software to design and develop the spatial database of system. The attributive data of agricultural region were gathered by collecting historical data and field survey, in which the geographic attributive data were stored into the spatial

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database, and the elementary attributive data were stored into the attributive database via Microsoft Access 2007. 3.1.2 DEM Disposal DEM were the models used to simulate change status of terrain wave by digital vector and grid formats. These data were disposed by grid simplified algorithm to control the level of detail (LOD) process, and form the different detail level of terrain data. 3.1.3 DOM Disposal DOM was composed of disposed digital navigation photo and satellite RS image, these data could be used to optimize the texture of 3D models and control background status of terrain by geometrical rectification and inlay. 3.1.4 DM Making DM were the 3D models with complicated structural characteristic, which were made by 3DS Max software, such as the models of building, road and field, etc. In order to make VDARMS have a better performance, models must be developed based on little triangle faces by polygon modeling function of 3DS Max software, additionally, vivid texture picture would be stuck onto the surface of models. 3.2 Construction of 3D Virtual Scene The main work in the virtual simulation module of agricultural region was concentrated on setting up 3D virtual scene, so the constructing procedure and fidelity of 3D virtual scene would directly affect the quality of system. Recently, there are many good 3D virtual simulation platforms at home and abroad, by comparing the platforms and considering the actual condition of agricultural region, VRMap 3.0 was selected as the scene driver of VRGIS which supplied key technologies as management of mass data, display of advanced simulation, communication among different platforms and database driver. In the procedure of constructing scene, the program could automatically create 3D virtual scene by using correlative parameters inputted by user in the attributive database (Table.1), and showed high reusability and flexibility. Table 1. Parameters of scene creation Parameter type Terrain creating parameters Object importing parameters

Background creating parameters Light creating parameters Camera adding parameters

Parameter name Scene name, terrain texture name, maximum and minimum longitude, maximum and minimum latitude Coordinate, object name and rotation, agro-region name, model name, conjunct code, numbers of transverse and longitudinal models, degrees of transverse and longitudinal excursions Sky radius, bottom color of sky, top color of sky, texture picture name of sky, texture picture name of ground Type, coordinate, angle

Plug name Terrain creating plug

Standard creating plug

Type, name, coordinate, angle


Standard importing and exporting plug



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3.3 Alternation between Digital Map and Virtual Scene Each object from agricultural region was distributed a code to be distinguished each other and make for managing and querying spatial information. These were stored into spatial and attributive databases via conjunct code, and used to realize the alternation between digital map and virtual scene. This function was designed under the query menu of virtual simulation module. The acting procedure was: (1) stored the code into the scene as the name of correlative 3D model; (2) acquired the name of 3D model by VRMap 3.0 SDK engine; (3) queried the information of correlative spatial object by SuperMap SDX+ engine, realized the function of real-time alternation. 3.4 Change of Crop Landscape The landscapes of crops show different forms at different growth stages and varieties. Considering the characters of crops in different growth stages, changing large-scale crop landscapes was realized by selecting the main growth stages to carry out the replacement for 3D models of fields. The acting procedure was: (1) first of all, added all the fields into the VRMapSelectionSet. (2) secondly, got the name, coordinate, rotation, zoom-adaption and remotion of one field object by IVRMapPMObjectDisp. (3) finally, replaced the 3D model of field with the obtained information by CreatePMObjectFrom3DS, and then transferred to another field until all the fields were changed. 3.5 System Development The VRGIS-based digital agricultural region management system was developed on the computer with Intel(R) Core (TM) 2 Duo CPU, 1G memory and Windows XP Professional operating system(Version: Chinese). By applying Visual Basic 6.0 to design the interface of system, SuperMap Deskpro 2008 to design spatial database, Microsoft Access 2007 to disign attributive database. The 3D models were set up on the platform of 3DS Max 2009 software, the modules of spatial information management and crop management plan design were developed by using the components of SuperMap Objects 2008 and existing crop management knowledge model, while the 3D virtual scene and the module of virtual simulation of agricultural region were established by VRMap 3.0 software.

4 System Application Case studies of the system were carried out in Heheng village of Jiangyan city and Qinglong village of Nanjing city, Jiangsu province, China. The executing procedure was: Firstly, the spatial and attributive databases were constructed with Google Earth images, digital photos and data resources via vectorization and tabulation. Secondly, the 3D models of building, road and field in the agro-region were made by 3D modeling software. Finally, all the data were imported into system. The application results indicated that the structure framework and design idea of VDARMS accorded with the

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development demand of modern agricultural information technology, realized the standard management and convenient query of information in agro-region, prescription design for crop management, and alternation between map and scene. At the same time, the function of virtual simulation also realized the automatic constructing and wandering of 3D virtual scene for rice and wheat in different growth stages (Fig.3-Fig.6).

Fig. 3. Main interface of VDARMS

Fig. 4. Alternation between map and scene in Heheng village


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Fig. 5. Prescription map of total nitrogen of rice in Qinglong village

Fig. 6. Virtual scene of rice jointing stage in Qinglong village

5 Discussion In order to realize the digitalization and visualization of information management in agricultural-region, a VRGIS-based digital agricultural region management system was developed with SuperMap 2008 as the platform of spatial information management and VRMap 3.0 as the driver of scene, and integrating with the existing crop model

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resources. The system had the functions as file management, spatial handling, information query, data analysis, prescription design for crop cultural management, virtual simulation and system maintenance, etc. Case studies of the system were carried out in Heheng village of Jiangyan city and Qinglong village of Nanjing city, Jiangsu province, the application result indicated that it accorded with the development of modern agricultural spatial information management, realized the standardization, digital management and visual display of agricultural information. The results provided a digital and visual platform for the construction and management of new countryside, exceedingly promoted the development of digital agriculture. Comparing with the existing agricultural spatial information management systems and virtual simulation systems(Shane et al., 2001; Ma, 2007; Liu et al., 2006; Jiang et al., 2008; Zou et al., 2006; Liu et al., 2006; Wu et al., 2008), the system has the following characters: (1) the display of spatial information is not only in the nonfigurative 2D space, but also in the dynamic and communicated 3D space, user can apperceive the real world by a intuitionistic manner; (2) by setting the parameters for the construction of scene and using the function of plug, the system can automatically and quickly construct the 3D virtual scene of agro-region. The weakness of costing much time in establishing 3D virtual scene and fixed applied object was overcame, and exceedingly promoted the reusability of virtual simulation technology. (3) the alternation of digital map and virtual scene eliminated the wildering sense in wandering 3D virtual scene, effectively showed the integrity of 2D digital map and the visualization of 3D virtual scene, and perfectly annotated the spatial information of agro-region. However, on account of the integration between GIS and VR based on data level, additional studies should be undertaken on the same data structure to realize the absolute integration; Further, considering the limitations of 3D GIS and computer technologies, the system should be updated by VRMap 4.0 or higher version of 3DGIS software to improve the efficiency of system running.

References 1. Liu, W.: The Origin and the Initial Practice of the Digital Agriculture. Agriculture Network Information 8, 21–23 (2005) (in Chinese) 2. Song, Z., Zhang, J.: Study Progress and Development Trend of Digital Agriculture. Modernization Agriculture 5, 1–4 (2007) (in Chinese) 3. Zhou, Y.: 3S Technique and Digital Agriculture. Bulletin of Surveying and Mapping 5, 69–71 (2009) (in Chinese) 4. Shane, R., Naiqian, Z., Taylor, R.K.: Development of a Field-Level Geographic Information System. Computer and Electronics in Agriculture 31, 201–209 (2001) 5. Ma, Q., Zhang, B., Zhang, C.: Developing and Studying of Spatial Information Management System of Digital Agriculture Based on COM GIS. Computer System Application 4, 86–89 (2007) (in Chinese) 6. Liu, X., Zhu, Y., Yao, X., et al.: WebGIS-Based System for Agricultural Spatial Information Management and Aided Decision-Making. Transactions of the CSAE 22(5), 125–129 (2006) (in Chinese) 7. Deng, H., Wu, F., Yin, C.: Virtual Reality Geographic Information System (VRGIS)-a New Field of the Research of GIS. Application Research of Computers 9, 33–35 (2002) (in Chinese)


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8. Jiang, J., Wen, X., She, G.: Research and Application of VRGIS in Forest Resources Management. Forest Research 21, 134–137 (2008) (in Chinese) 9. Zou, J., Zou, Z., Zhou, C., et al.: Study on Large-Scale City VR Simulation System and its Realization. Journal of System Simulation 18(8), 2199–2202 (2006) (in Chinese) 10. Liu, J., Yu, H., Han, Y., et al.: Development of Tourist Area Virtual Simulation System Based on VRMap. Journal of System Simulation 18(1), 130–133 (2006) (in Chinese) 11. Wu, H., Zhong, X., Zhao, C., et al.: Realization of the Dynamic Interactive 3D Virtual Wandering System in the Rural Community Based on VRML. Transactions of the CSAE 24(2), 176–180 (2008) (in Chinese) 12. Cao, W.: Digital Farming Technology. Science Press, Beijing (2008) (in Chinese) 13. Huang, X., Ma, J., Tang, Q.: An Introduction to Geographic Information System. Higher Education Press, Beijing (2001) (in Chinese) 14. Hu, Z.: Implementation of Shenzhen Private House 3D Demonstration System. Geomatics and Spatial Information Technology 30(3), 133–135 (2007) (in Chinese)

Design and Simulation Analysis of Transplanter’s Planting Mechanism Fa Liu, Jianping Hu, Yingsa Huang, Xiuping Shao, and Wenqin Ding Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education&Jiangsu Province, Jiangsu University, Zhenjiang 212013, China [email protected]

Abstract. A new-style transplanter’s planting mechanism was designed, which was composed of Planetary Gear, Planetary Carrier, Connecting rod, Groove cam and Planting arm. Built the kinematics model and determined the main parameters which influenced the Plant-arm’s locus by analyzing of the Kinematic model. Created the 3D Model in PRO/E and imported it into the Kinematics simulation software ADAMS, analyzing Groove-cam’s offset angle, Connecting-rod’s length and its impact on the Plant-arm’s kinematics locus. The effect laws of the structural parameters on the Plant-arm’s locus were obtained through analyzing the Plant-arm’s locus, which got by changing the groove cam’s horizontal offset angle ranging from 0° to 20°, the sum of Connecting-rod’s length ranging from 130mm to 150mm, and the subtract of Connecting-rod’s length ranging from 15mm to 25mm. The analysis results are of theoretical significance to the dimension synthesis and optimization design. Keywords: Transplanter, planting mechanism, ADAMS, Groove cam, Plant-arm’s kinematics locus.

1 Introduction Raise seedling and transplant can increase crop production in every unit area, and make upgrowth ahead of time, which can withstand gale, harmful rain, low temperature, and other nature disaster. Besides, it also saving seed. Seeds are usually grown under the film by farmer in many place, because it is useful to improve the soil temperature, keep moisture and restrain weeds. The nacelle-type and dibble-type transplanting mechanism are good mechanism for transplanting film, but they are not convenient and safe to directly drop seedling and they are easy to leave out seedling when the machine are operated and their work efficient are low when seedling are transplanted over the larger areas. Now a simple and credible type of transplanting mechanism was designed, which was easy and convient to be operated and adjusted. the efficiency of the machine was much more greatly improved. There was less seedling to leave out in work progress. The three dimension model of the transplanting mechanism was establish in pro/e software. The relationship between the planting arm’s locus and structural parameters was analyzed by the mechanical simulation software ADAMS in this paper. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 456–463, 2011. © IFIP International Federation for Information Processing 2011

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2 Kinematic Analysis of the Planting Mechanism 2.1 Structure and Working Principle of Planting Mechanism A transplanter’s planting mechanism was shown in Figure 1, which was composed of planetary gear, planetary carrier, link, groove cam and planting arm. The numbers of sun-wheel’s teeth was twice times that of the planetary gear’s teeth. The cam groove was divided into two parts and a angle-off between the two part. Planting arm, fixed on the connecting rod 2, moved with the connecting rod 2.

1. central gear 2.planetary carrier 3.planetary gear1 4. Planetary gear2 5.connecting rod1 6.connecting rod2 7.roller 8. Groove cam 9.planting arm Fig. 1. Planting mechanism structural chart

2.2 Transplanter’s Planting Mechanism Kinematics Equations The initial position of planting bodies was shown in Figure 1. The Cartesian named xO1y established as shown in fig.1. The Coordinate equation of the planetary gear’s center O2 is: ⎧⎪ x O2 = l1 cos( α 0 + ω t ) ⎨ ⎪⎩ y O2 = l1 sin(α 0 + ω t )

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The Coordinate equation of the connecting rod’s endpoint A is:

⎧⎪ x A = xO2 + l2 cos( β 0 + ω t ) ⎨ ⎪⎩ y A = yO2 + l 2 sin( β 0 + ω t ) The Coordinate equation of planting arm’s endpoint B is:

⎧ xB = x A + l3 cos γ ⎨ ⎩ yB = y A + l3 sin γ The Coordinate equation of planting arm’s endpoint D is:

π ⎧ ⎪⎪ xD = xB + ρ cos(θ + 2 − γ ) ⎨ ⎪ y = y − ρ sin(θ + π − γ ) B ⎪⎩ D 2 The rate equation of the planetary gear’s center O2 is:

⎧⎪ v xO 2 = ω l1 sin(α 0 + ω t ) ⎨ ⎪⎩ v yO 2 = ω l1 cos( α 0 + ω t ) The endpoint A of the connecting rod 1 rate equation is:

⎧⎪ v x A = v xO2 + ω l 2 sin( β 0 + ω t ) ⎨ ⎪⎩ v y A = v xO2 + ω l2 cos( β 0 + ω t ) The rate equation of the planting arm’s endpoint B is:

⎧⎪ v x B = v x A + ω 2 l3 sin γ ⎨ ⎪⎩ v y B = v x A + ω 2 l3 cos γ The rate equation of the planting arm’s endpoint D is:

π ⎧ ⎪⎪vxD = vxB − ω2 ρ sin(θ + 2 − γ ) ⎨ ⎪v = v + ω ρ cos(θ + π − γ ) xD 2 ⎪⎩ yD 2


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i

ω2 = γ α0——the initial angle between the planetary carrier and the horizontal β0——the initial angle between connecting rod 1 and the horizontal γ ——the angle between connecting rod 2 and the horizontal θ —— CBD l1 ——the length of the planet carrier l2 ——the length of the connecting rod 1 l3 ——the length of the connecting rod 3 ω——the angular velocity of the planet carrier

∠

3 Virtual Prototype Model of the Planting Mechanism The planting mechanism’s three-dimensional model was established and assembled in the pro/e and then transmited it into the mechanical simulation software Adams. The Planting mechanism’s Virtual Prototype Model was established in the mechanical simulation software Adams as in Fig.2.

Fig. 2. The Planting mechanism’s Virtual Prototype Model

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4 Kinematics Simulation 4.1 Planting Mechanism’s Simulation in Different Structural Parameters The shape of the planting arm’s locus was the chief factor to effect the function of the transplanter. The groove cam’s horizontal offset angle, the sum of Connecting-rod length and the subtract of Connecting-rod length were the main factors to effect the Plant-arm’s locus by analyzing the transplanter’s planting mechanism kinematics equations. So we built the Virtual Prototype Model of the Planting mechanism in the different structural parameters of the groove cam’s horizontal offset angle, the sum of Connecting-rod length and the subtract of Connecting-rod length. The Planting arm’s locus were got by the different Virtual Prototype Model’s simulation as in Fig.3, Fig.4 and Fig.5. Structural parameter values in Table 1. Table 1. Structural parameter A (groove cam’s horizontal offset angle)

B (sum of Connecting-rod length)

C (subtract of Connecting-rod length)

1

0°

0mm

120mm

2 3 4 5

5° 10° 15° 20°

10mm 20mm 30mm 40mm

130mm 140mm 150mm 160mm

Fig. 3. Planting arm’s locus in different groove cam’s horizontal offset angle


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Fig 3, Fig4 and Fig 5 is the planting arm’s locus in different structural parameters respectively. 100

50 A3B1C3 0

A3B2C3

-50

A3B3C3

height (mm) -100

A3B4C3 A3B5C3

-150

-200

-250

-300

-350

0

50

100

150

200

250

300

length(mm)

Fig. 4. Planting arm’s locus in different subtract of Connecting-rod length

50 A3B3C3 A3B3C2 0

A3B3C5 A3B3C4

A3B3C1

-50 height (mm) -100

-150

-200

-250

-300

0

50

100

150 200 length(mm)

250

300

Fig. 5. Planting arm’s locus in different sum of Connecting-rod length

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4.2 The Simulation Results of Planting Mechanism Were Analyzed in Different Structural Parameters The shape of the planting arm’s locus was determined by the width of planting arm’s locus, the height and the deflection angle. So we selected the width of planting arm’s locus, the height and the deflection angle as the evaluation criteria of planting arm’s locus in this paper. The locus of the planting arm’s was got by the simulation of the planting mechanism’s virtual prototype model was in different structural parameters. Shown in Table 2. a—the width of planting arm’s locus. The planting distance was influenced by the value of a. b—the height of planting arm’s locus. The Planting Depth was influenced by the value of b. c—the deflection angle of planting arm’s locus, It was the angle between the horizontal and the line connecting the lowest point and the highest point of the locus. The stability of catching seedling were influenced by the value of c. Table 2. Simulation results analysis Structural parameters

A1B3C3 A2B3C3 A3B3C3 A4B3C3 A5B3C3 A3B1C3 A3B2C3 A3B3C3 A3B4C3 A3B5C3 A3B3C1 A3B3C2 A3B3C3 A3B3C4 A3B3C5

Results Fig.

Fig.3

Fig.4

Fig.5

Width(a)

Indicator value Length(b)

Declination(δ)

93 97 97 96 98 25 49 97 138 183 94

276 281 292 315 335 315 300 292 326 336 256

0° 0.6° 3° 4.4° 7.9° 4.5° 1.9° 3° 9° 12° 4.5°

93 97 93 94

286 292 330 360

5.8° 3° 4.4° 5.6°

As is shown in Table 2, Fig.3 shows a set of curves of a width of about 97mm, a height ranging from 276mm to 335mm and a declination angle ranging from 0° to 7.9°. Fig.4 shows a set of curves of a width ranging from 25mm to 183mm, a height ranging from 292mm to 336mm and a declination angle ranging from 1.9° to 16°. Fig. 5 shows a set of curves of a width of about 94mm, a height ranging from 256mm to 360mm and a declination angle ranging from 3° to 5.6°.


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5 Conclusion (1) The width of planting arm’s locus depends on the subtract of Connecting-rod length. With the subtract of Connecting-rod length increasing, the width of planting arm’s locus became wider and wider. (2) The height of planting arm’s locus depends on the sum of Connecting-rod length, the subtract of Connecting-rod length and the groove cam’s horizontal offset angle. With the sum of Connecting-rod length, the subtract of Connecting-rod length and the groove cam’s horizontal offset angle increasing, the height of planting arm’s locus became larger and larger. (3) The deflection angle of planting arm’s locus depends on the groove cam’s horizontal offset angle and the subtract of Connecting-rod length. With the groove cam’s horizontal offset angle and the subtract of Connecting-rod length increasing, the deflection angle of planting arm’s locus width of planting arm’s locus became wider and wider.

Acknowledgements This work was financially supported by the three agricultural machinery project of Jiangsu Province for 2008.

References [1]

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Wang, W., Dou, W., Wang, C.: Parameter Analysis of the planting Process of 2ZT-2 Beet Transplanter. Transactions of the Chinese Society for Agricultural Machinery 40(1) (2009) Li, Q., Wang, Z.: Main structure parameter and analysison planting apparatus with twin conveyer belt. Transactions of the Chinese Society for Agricultural Machinery 28(4), 46–49 (1997) Dong, F., Geng, D., Wang, Z.: Study on block seedling transplanter with belt feeding mechanism. Transactions of the Chinese Society for Agricultural Machinery 31(2), 42–45 (2000) Li, Q., Lu, S., Li, L.: Experimental study on a slideway parting-bowl-wheel transplanter. Transactions of the Chinese Society for Agricultural Machinery 32(2), 30–33 (2001) (in Chinese) Feng, J., Qin, G., Song, W., et al.: The kinematic analysis and design criteria of the dibble-type transplanter. Transactionsof the Chinese Society for Agricultural Machinery 33(5), 48–50 (2002) Zhou, D., Sun, Y., Cheng, L.: Design and analysis of a supporting-seedling mechanism with cam and combined rocker. Transactionsof the Chinese Society for Agricultural Machinery 34(5), 58–60 (2003) Yu, G., Zhao, F., Wu, C., et al.: Analysis of kinematic property of separating-planting mechanism with planetary gears. Transactions of the Chinese Society for Agricultural Machinery 35(6), 55–57 (2004) (in Chinese) Li, Y., Xu, L., Chen, H.: Improved design of spade arm in 4YS-600 tree transplanter. Transactions of the CSAE 25(3), 60–63 (2009) (in Chinese and English abstract)

Design and Simulation for Bionic Mechanical Arm in Jujube Transplanter* Yonghua Sun, Wei Wang**, Wangyuan Zong, and Hong Zhang Zibo in Shandong, Lecturer, Mechatronics Tel.: 0997-4683859 13031270332 [email protected]

Abstract. In this paper an automatic bionic mechanical arm of jujube transplanter has been designed and simulated with Pro/E and ADAMS software. The device can achieve the work of clamping—sending—setting the sapling and support the sapling to guarantee it perpendicularity in setting process. Design the structure of manipulator utilizing the simulation of hand working. There is 5-DOF at the manipulator to achieve simulating. Constitute dynamics mathematical model and estimated inseminate error of bionic manipulator. The three dimensional model of the manipulator was build up and simulated by using Pro/E software. Then up build the virtual prototype and kinetics simulation in ADAMS software and chalk up the dynamics parameter curve of clamping force etc. This manipulator will establish theory and practice foundation to the cyber-identify and cyber-supervise of sapling translating.

1 Introduction Mechanical arm have been designed for jujube transplanter aiming at the outside diameter and characteristic of tree form, to meet new requirement in south Xinjiang, featured with new planting mode that called lower stem and high-density. It is designed by simulating the supporting mechanism of human hand. This new design can improve planting stability and planting efficiency, under the help of the support from mechanical arm. The mechanism is the core component of the transplanter that directly affects the quality of seedlings planted.

2 Working Principle and Structure The bionic mechanical arm is composed by structures of upper and lower splint (5, 7), link (4), spring (3), roller (2, 8) and link (1) and pin etc. There is five-freedom except the rollers that can let the manipulator realize the opening, closing, turning as well as other operations. *

Project Funding: Subsidized by Sinkiang Science and Technology Supporting Projects (2009zj19) ** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 464–471, 2011. © IFIP International Federation for Information Processing 2011

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Fig. 1. Structure Diagram of Bionic Mechanical arm 1. Chain-Element 2.First Roller 3.Spring 4.Link 5.Upper Splint 6.Sapling 7.Lower Splint 8.Second Roller

Fig. 2. Schematic Diagram of Bionic Mechanical Arm

In working, chain-element (1) link to the driving chain and moving at a certain speed. The first roller (2) and second roller (8) run in each orbit. The manipulator is supported by the second roller (8). The first roller (2) able to automatic control the opening and closing of upper splint (6) is located at the obit with variable size and fitted to spring (3). Figure 1 is location of spring at the state of compression and now the manipulator will keep current state for a minute until completed sapling planted. Then the upper splint (6) will detach the sapling with opening angle θ by spring force as the change in size of guide and wait for the next operation. Figure 2 shows the working principle. The opening angle θ of hand is controlled by a long slot between guide and strut. Parallel mobile is main movement of link (4) which attach to the relevant parts of lower splint by hinge pin.

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3 Motion Mathematical Models There will be a little offset error to sapling`s right location in actual planting process because of linker in structure according to the working principle of the bionic mechanical arm. Hypothesis the diameter of sapling be handed is d, and the distance that link moving down is h, now, the dip of upper splint is θ. Ideally, the field angle is θ between the two hand splint if there is no movement of lower splint. Ideally, the field angle between two splints is β=θ-α If considering the dip α of lower splint as the structure factor. That is the error angle is β=θ-α.

Fig. 3. Working Locus of Manipulator

Fig. 4. Error Coordinates in Sapling Planted of Manipulator

Define the original point o as the coordinate origin to analysis the offset of sapling planting. Establish the coordinates shown in Figure 4. The coordinate sapling planted is (x1,y1) in ideally and the actual coordinate is (x2,y2) after considering the coordinate error if hypothesis the original point as (-Ox,-Oy) after migrated. We can get the follow mathematic model according to its trajectory.


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The coordinate sapling planted in ideally:

β ⎧ ⎪ x1 = d × cot 2 ⎨ ⎪⎩ y1 = d The actual coordinate:

d cos(α + β / 2) ⎧ ⎪ x2 = sin(β / 2) − Ox ⎪⎪ ⎨ ⎪ d sin(α + β / 2) ⎪ y2 = + Oy ⎪⎩ sin( β / 2) The mathematic model of sapling planted error in actually: d cos(α + β / 2) ⎧ + Ox ⎪Δx = −( x2 − x1 ) = d cot(β / 2) − sin(β / 2) ⎪ ⎨ ⎪Δy = y − y = d sin(α + β / 2) − d + Oy 2 1 sin(β / 2) ⎩⎪

Δx, Δy were sapling planted error in x, y direction.

4 Dynamic Simulation Analysis 4.1 Virtual Prototype Design Based on Adams

Figure 5 shows the virtual prototype designed in ADAMS which comes from the three-dimensional model of bionic mechanical arm established in PROE. Set up the

Fig. 5. Modeling of Virtual Prototype

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Fig. 6. Dynamic Simulation of Virtual Prototype

attribute as materials of various parts as stainless unity and so on. Set the connect sets between the parts and load the property of spring such elastic stiffness coefficient as K=800 and the damping coefficient as C=0.5. Then to exert the force F in the connecting rod end and set the running time as 0.03s and the step as 50, and then operate it to perform the dynamic simulation for the prototype. Afterward, we will obtain a motion state diagram likes Figure 6. 4.2 Kinematics and Dynamic Simulation Analysis

Change the size of the force F in connecting rod end at the same kinematics time and measure the angle caused by compression force on the spring, field angle of the upper and down hand splint and sapling planted position error. Then get the variation curve of various spring force F, field angle-1 and error angle-2 respectively in the time of t=0.03s. Follow the Figure 7, Figure 8, Figure 9.

Fig. 7. Variation Curve of Spring Force in Time on Different Force F


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Fig. 8. Variation Curve of Field Angle of Plant Holder`s Hand on Different Force F

Fig. 9. Variation Curve of Planted Error on Different Force F

Select the preloading as 0.2~0.3N according to the curve analysis of each parameter mentioned above and its operating characteristics and requirements. Now the field angle can meet the demand for planting seedlings and have a little error. Therefore, the hand field angle 30° able to be the limit position to improve the relative motion quantity of the link. Analysis the position change, velocity and the acceleration of the load component force in X direction at the time that exert the force 0.3N in connection rot as the preload force and get the variation curve as Figure 10. The load component force is more stable in the intermediate section. Analysis the elastic, change speed and amount of compression of compression spring and get the variation curve as Figure 11. Table 1. Kinetic Parameters in Prescriptive Time on Different Force Kinetic Parameters Preloading˄N˅ 0.1 0.2 0.3 0.4

F(spring) ˄N˅ 5.948 4.758 3.569 2.379

Angle-1 ˄°˅ 32.71 29.27 26.40 24.17

Angle-2 ˄°˅ 22.71 13.55 7.119 2.978

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Fig. 10. Variation Curve of Preload in X Direction

Fig. 11. Variation Curve of Elastic of Compression Spring

5 Conclusions In this paper, automatic mechanical arm which imitates human hand to support sapling is designed by using new operation principle of the manipulator. Analysis and establish mathematical model of the planted error to meet requirement of the jujube transplanting demand for lower-density inseminate mode. Design the corresponding structure of the manipulator use of above mathematical model. Build modeling in PROE, introduce it into ADAMS and establish virtual prototype in it. Do the simulation experiment for primary related parameters of the prototype and draw the corresponding variation curve. The clamping force to sapling is not much according to the curve analysis of each dynamic parameter of the virtual prototype of planted manipulator, so it can realize base on the elastic of spring. Furthermore, clamping force can be adjustable according to the diameter of the sapling, with the adaptive ability of spring during the working process.

References [1] [2]

Zhang, M., Li, S.: Optimum Planting Depth to Poplar Mechanical Afforestation in Subarid Sand, vol. (4), pp. 4–5. Jilin Forestry Science and Technology, Changchun (2004) Dong, Q., Han, L.: Cultivation Techniques for High Yield of Jujube in Sandy. Inner Mongolia Forestry Investigation and Design, vol. (4), pp. 83–84. Forestry Survey & Design Institute, Hohhot (2008)

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Li , J., Xiao, H., Hu, Z.: Kinematics Simulation of Mechanical Arm Based on ADAMS. Machine Tool & Hydraulics (8), 206–209 (2009) Feng, S., Xie, J., Zhu, W., Ma L.Z.: The Motion Control Study of The Automatic Transplanting Robot. Machinery Design & Manufacture (3), 166–168 (2008)

Design for Real-Time Monitoring System of High Oxygen Modified Atmosphere Box of Vegetable and Fruit for Preservation Zhanli Liu1, Congcong Yan1, Xiangyou Wang1,* and Xiangbo Han2 1

School of Agriculture and Food Engineering, Shandong University of Technology, P.R. China 2 College of Computer Science and Technology, Shandong University of Technology, P.R. China [email protected]

Abstract. According to the disadvantages of traditional box for preservation, including low oxygen and high carbon, a new control system of high oxygen is designed. This paper presents the design and implementation method in this system. The system combines traditional PID control with fuzzy control which can adjust parameters of the whole system automatically. The box can control the dynamic content of oxygen and carbon dioxide, and monitor nitrogen flow, temperature and humidity real-timely so that preservation time can be prolonged. It also can collect and keep the data of the dynamic content of oxygen and carbon dioxide which suits for fruit and vegetable for preservation. The former environment can be reappeared when need. The system works steadily and has strong functions. Keywords: High oxygen; Modified atmosphere box; Real time monitor; Design.

1 Introduction Day, the British scholar for the first time made clearly the application of high-oxygen (>70% O2) in modified atmosphere packaging of fresh-cut vegetables and fruits in 1996. Domestic and foreign research of high-oxygen (21%-100% O2) on the effect of postharvest gradually increased, and the treatments of high oxygen are expected to play an important role in fruit and vegetable storage[1]. Research has shown that high-oxygen treatment of fruits and vegetables can reduce the respiration and ethylene production, slow their browning, and improve the preservation effects[2-4]. The application of high-oxygen or even pure oxygen modified atmosphere technology on fruit and vegetable preservation has aroused close attention. So it is necessary to make a study of a reliable and stable high oxygen modified atmosphere control system for further development of high-oxygen fresh-keeping equipment. However, the impact mechanism of the high oxygen on the postharvest physiology and quality is not indepth, so it severely limits the development of high oxygen storage[5]. Thus, most of *



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high oxygen experiments lack of accurate monitoring of oxygen, carbon dioxide, nitrogen flow rate, temperature and humidity, and can not control stably the environment of high oxygen. In this paper, the self-made high oxygen experimental apparatus were studied, and a data acquisition system and related test equipment were developed providing a scientific basis for high oxygen modified atmosphere theory.

2 Experimental Device of High Oxygen Modified Atmosphere Box Central processing section formed by a microprocessor is the heart of the whole control system, which completes the functions of data acquisition and processing, keyboard and display processing and system control. Temperature and humidity transmitter is mainly to measure gas concentration of the tested environment, and output the standard current signal corresponding to the surrounding gas concentration after the signal processed by the internal data processing. Gas flow meter part is mainly to measure the volume of the input nitrogen. It can select 4-20mA output, level output, or pulse output module according to needs. Analog multiplexer switch is mainly to turn the switch channel between separate ways analog and A/D converter, allowing one way analog signal input to the A/D converter in a specific period of time, and it can achieve the purpose of time-conversion to reduce the number of A/D converter and reduce costs. Current transfer voltage circuit is used to change the transmitter and flow meter standard output current signal into the corresponding voltage signal and conduct the follow-up signal processing. The role of sample retainer is used to maintain analog signal voltage of the A/D converter input constant during the conversion period, then ensure a higher conversion precision, and greatly increase the collection frequency of data collection. Some data memory stores the measured data immediately and preserves the measured environmental data for long-term. And data can be transferred out to external memory by a data interface, facilitating accurate analysis. Optical isolation part completes the isolation and amplification from weak signal to strong signal. The conversion of manual control and auto-control can be set by Panel. In the case of automatic failure or manual intervention (for example, using the manual control can quickly reach steady state before reaching the set high oxygen concentration), some manual control part can control all of the conditioning systems of the atmosphere storage environment. Program memory is used to store system procedures. Solenoid valve is used to control nitrogen access.

3 Working Principle of High Oxygen Modified Atmosphere Box The concentration of oxygen and carbon dioxide, temperature and humidity of box are measured by the corresponding transmitter. Transmitter output signal is transmitted to the microprocessor after a series of treatments, and compared with the set initial values, when any kind of gas concentration is below the set value, appropriate instructions will be issued by the microprocessor, then the electric value corresponding is opened with the gas to let that kind gas in. By controlling the opening of the electric valve to control the air flow rate, when the concentration closes to the set value, the microprocessor controls the corresponding procedure to reduce the opening to prevent the excessive ventilation and avoid its concentration exceeding the set value. When the value of

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any gas concentration is higher than the set alarm value, the system alarms, and the corresponding unit of the micro-processor will give appropriate instructions, so that the corresponding electric valve of the gas will be closed, and the nitrogen valve will be opened, by inletting into constant flow of nitrogen the concentration of this gas will be reduced. When the concentration of this gas closes to the set value, the microprocessing time relay control valve will close for 10 seconds, leaving some buffer time to avoid excessive nitrogen filled. Then process it according to the measured actual situation. The gases coming out from nitrogen generator, oxygen, and carbon dioxide bottles pass into the inlet pressure regulator box, and then pass into the test box. The gas cylinder export pressure of the nitrogen generator, oxygen and carbon dioxide bottles can be set much higher, and then adjust the inlet air pressure to the required. This can avoid failing to reach the export settings when the cylinder pressure is insufficient, and the pressure can be precisely adjusted by the intake air pressure tank.

4 Data Acquisition System Oxygen sensor is most important one of all the sensors of the system, and it has a very big impact on the system, so its selection is of great importance. It must meet the fast, reliable and accurate measurement of the oxygen concentration. Now commonly used oxygen sensor is electrochemical sensors. According to the principle, CO2 sensor can be divided into electrochemical CO2 sensor and infrared CO2 sensor, the former is cheaper, but the accuracy is lower, and life is shorter, and preheat time is longer than the latter. Compared to carbon dioxide transmitter, carbon dioxide sensor is low precision, poor linearity, less function, and also needs to set up a dedicated signal processing devices for filtering and V/I conversion for processing, so the costs are increased. In view of the advantages of the transmitter, we use the transmitter (with imported high-grade infrared sensor) to replace the specialized sensors.

5 Experimental Debugging Agaricus bisporus were used as the materials to test the stability of the high oxygen experimental box. The concentrations of oxygen and carbon dioxide were set

Fig. 1. Change trends of oxygen in box

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Fig. 2. Change trends of carbon dioxide in box

respectively to 75% and 25%, and stabilized at a predetermined value (fig. 1 and fig. 2). From the results, the high oxygen modified box can real-time control the dynamic contents of oxygen and carbon dioxide. Acknowledments. This study was supported by the National Natural Science Foundation of China (No. 30871757).

References [1] [2]

[3]

[4]

[5]

Zheng, Y.H.: Superatmospheric oxygen and postharvest physiology of fresh fruits and vegetables. Plant Physiol. Comm. 38(1), 92–97 (2002) Li, P.X., Wang, G.X., Liang, L.S., et al.: Effects of high oxygen treatments on respiration intensity and quality of ‘DongZao’ jujube during shelf-life. Transactions of the Chinese Society of Agricultural Engineering 22(7), 180–183 (2006) Escalona, V.H., Verlinden, B.E., Geysen, S., et al.: Changes in respiration of fresh-cut butterhead lettuce under controlled atmospheres using low and superatmospheric oxygen conditions with different carbon dioxide levels. Postharvest Biology and Technology 39, 48–55 (2006) Conesa, A., Verlinden, B.E., Artés-Hernández, F., et al.: Respiration rates of fresh-cut bell pepper under superatmospheric and low oxygen with or without high carbon dioxide. Postharvest Biology and Technology 45, 81–88 (2007) Liu, Z.L., Wang, X.Y., Zhu, J.Y., et al.: Progress in effects of high oxygen on postharvest physiology and quality of fresh fruits and vegetables. Transactions of the Chinese Society for Agricultural Machinery 40(7), 112–118 (2009)

Design of Agent-Based Agricultural Product Quality Control System Yeping Zhu, Shijuan Li, Shengping Liu, and E. Yue Laboratory of Digital Agricultural Early-warning Technology of Ministry of Agriculture of China, Institute of Agricultural Information, CAAS, 100081 Beijing, China {zhuyp,lishijuan.spliu,eyue}@mail.caas.net.cn

Abstract. Aiming at the problems existed in agricultural product quality control, management and traceability such as the considerable influence of human factors, weak ability of handling emergencies and lacking of support of intelligent key technologies, this study explores the application of agent theory, method and technique to solve the problems of process control, traceability, intelligent information watching and information technology application to emergency reaction conditioned on the general characteristics from production to circulation. In this study agent application and development method will be put forward. By means of investigating its communication and cooperative mechanism, we design the agent-based universal system framework for quality control of agricultural product. According to production and processing characteristics of different agricultural products, corresponding controlling unit and condition are increased to adapt to the special aim. Keywords: Agent, Agricultural product, Quality control.

1 Introduction As the important problem which concerns the masses most, food safety affects people’s health and life, involves the economic healthy development and social stabilization. How to control and manage food safety effectively has been becoming a research focus in recent years. The watching and effective management to product flow of farm produce can not only settle the problems of quality control and information delivery existed in every link such as production, processing, transportation, storage and sales, but also protect native agricultural product market and food safety. Europe began quality monitor and control in stockbreeding long ago. After the study and evolvement year by year, now relative perfect system has been come into being [1-3]. Since 21st century china gradually strengthened the study on food safety control method and system. A series of relative standards and guides have been established. Zheng Fengtian and Zhang Yongjian et al proposed that china must set up food safety system [4, 5]. Ye Yongmao considered the compellent food safety standard system should be constituted by means of reforming food safety management and operational mechanism and enhancing food safety legislation [6]. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 476–486, 2011. © IFIP International Federation for Information Processing 2011

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The author ever studied bee production quality traceability and developed software system [7, 8 9]. In the process of agricultural product quality control and traceability, man-made factors play an important role. Being lack of intelligent key technologies, although some technologies such as sensor, radio frequency identification and image recognition had been adopted in information collecting [10-15], database management, query and analysis still are the main method in agricultural product quality control. So there are no enough technologies involved in quality control to reply emergency. The difficulties existed in information collection, tracking and control for the small agricultural product, which need to be blended such as foodstuff and bee production, demand the corresponding information technologies as support to settle the key issues and reduce the effect of man-made factors. In this paper a method to control agricultural product quality with the characteristics of perceptivity, intelligence and cooperation will be studied. It provides technology support and universal resolution for control and traceability of such agricultural products as grain, bee product, vegetable and fruit etc., which are small, distinctly characteristic of dispersive production and need to be combined processed.

2 Status and Analysis of the Study The concept of agent was promoted in the end of 1970s, and the study of its methods and implementations has been developed in an active period. As one research domain of the distributed artificial intelligence, agent theory and technology have aroused a great deal of attention because multi-agent system (MAS) plays an important role in modern computer science and its application. In a multi-agent system any agent needs to communicate and cooperate with the other agents. Its behavior and decision vary with the other agents and conversation rules. The universal language-behavior theory formal language is used to make the agents in communication understand their respective inner state and purpose [16-25]. The application of MAS technology in agriculture is less than that in other fields. Liu Huimin at Capital Normal University studied multiple collaborative approaches based on the analysis of MAS technologies and theories, promoted an implementation plan for the MAS coordination mechanism based on Web Services technology to provide effective method and implementation technology [26]. Through the analysis on agricultural expert system and its characteristics, Yang Yan put forward the design project of web-based agricultural expert system. Xue Ling at Peking University and Chinese Academy of Agricultural Sciences applied agent to the study on modern agricultural economic management and decision-making system, provided the structure and components of Agriculture Economy Intelligent Decision Support System (AEIDSS), analyzed the implementation method of classifier-based agent, and discussed the work principle of dynamic analysis, evaluation, forecasting and optimization under multi-agent communication and cooperative environment. The author also designed the Agent-based Cooperative Analysis and Decision Support System for Regional Agricultural Economic Information [27-30].

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With the issue of “Food Safety Law” in 2009, china governments and departments all levels increased the construction of agricultural standard system and the control power of agricultural product market, and a multitude of production bases for high quality agricultural products have come into service [31-37]. China, as a large country for production area and yield, not only is a production country for agricultural products, but also is a consumption country. So how to make the best of existing agricultural information network and comparative advanced technologies to provide information service of agricultural products quality, safety, standard and trademark etc. in order to boost the information construction and control method study of agricultural products quality safety is important and impendent. Building agricultural products traceability system is an inevitable trend for world agriculture, and traceability system has been becoming an important development direction. No study directly concerns the agent application on agricultural products quality safety and control except only a few reports about key technology and quality inspection method. Dmytro Tykhonov et al described a kind of multi-agent imitation model for trust tracking game. Trust tracking game simulated the game player, was a research measure to collect the human behavior data in the food supply chain with the characteristics of asymmetry food quality and safety information [38]. Dr Eleni Mangina and Ioannis Giavasis provided a multi-agent system to supervise gellan gum production, which included online data collection, forecasting the future benefit by capturing historical data automaticly [39]. Moises Resende-Filho mentioned a sort of commission surrogacy model to encourage food safety tracking system. His study indicated the more reliable tracking system make the dealer attach more importance to food safety; the inapposite tracking system couldn’t inspirit the dealer to use the safe material in food industry [40]. Zhu Li and Wang Haiyan used the theory and principle of HACCP to discuss how to build food safety quality control system in food supply chain of Chain Supermarket [41]. Deng Ning et al tried to apply the core concept of agent on supply train risk control system, and proposed the framework mode to effective manage supply chain risk [42]; From the angle of quality guarantee Xiao Yuan and Liang Gongqian analyzed the conflicts among enterprises in supply chain, put forward the quality supervision mechanism. Multi-agent technology was applied to modeling for quality supervision system, and the system structure was provided [43]. Li Feng studied how to collect economically the real-time information of transporting goods and send to the back-end server (logistics information system). Radio Frequency Identification (RFID) was used by mobile front end subsystem to retrieve information of goods in automatic fashion, and the corresponding agents fulfilled the data processing and delivery. Back-end server also was built on mobile agent. This can not only ensure the customization of information collection, but also increase the system opening [44]. The increased demand on innovating food safety and quality control method offers a chance for agent technology with characteristics of sociality, autonomy, intelligence and mobility. MAS technology provides a method and measure for collaboration integration to guarantee quality, offers a feasible scheme for running quality supervision system successfully. Appling agent technology to study agricultural product quality safety and control method has far-reaching meaning on food safety and citizen health, at the same time will provide a new research method.


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3 Design of Agent-Based Agricultural Product Quality Control System First of all, let’s analyze the traditional flow of production, processing, sales, quality tracking and traceability for agricultural product (Fig. 1). In quality traceability database is the kernel technology. Figure 2 shows agent-based agricultural product quality control flow which core is the agent cooperation and intelligent control. The information delivery, task assignment and cooperation in whole system are on the control of information management agent and task management agent.

Fig. 1. Traditional flow from production to sales of agricultural product and quality traceability

Agent-based agricultural product quality traceability ensures the quality control from the source of supply chain. Production information agent, purchase information agent, processing information agent, sales information agent, circulation information agent guarantee the communication with each node on supply chain and the responsiveness and veracity of information collected. System will feed back the corresponding behavior to task management agent according to traceability demand. Task management agent communicates with each agent concerned and delivers the results to consumer. Agent-based agricultural product quality traceability and control is

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bestowed with unique advantages. It can realize the real-time information collection and intelligent processing, make purchase, dealer and enterprise obtain the product and industry information so as to adjust their management, offer related traceability information to the consumer.

Fig. 2. Agent-based agricultural product quality control flow

3.1 System Architecture The multi-agent system to control agricultural product quality through the whole course includes several agents such as information management agent, task management agent, tracing and traceability agent, market analysis and forecast agent, logistics management agent, instant event monitoring agent, emergency treatment agent and data mining agent and so on. System architecture is based on multi-agent management platform. Agentoriented thinking mode and cooperative evolvement theory replace the traditional structural design and object oriented design methods. System is composed of five parts i. e. user, communication and task sales, task resolving, information management and basic information (Fig. 3). Each part includes many agents which do their own work.


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Fig. 3. Architecture of agent-based agricultural product quality control system

3.2 Function Design of System User management agent. Interface agent accepts the task requests from users and delivers to task management agent. The results from multi-agent system are sent back to interface agent through task management agent. User interface agent creates different interface including drawing, table, text and figure depending on the different tasks. Information management agent. Information management agent is responsible for collecting and managing the information from production to sales for example information modification, storage and delivery. Task management agent. After receiving the requests from users, task management agent assigns distinct tasks to different agents for instance information analysis, production information collection task, tracing and traceability, logistics management, market analysis, instant event monitoring, emergency treatment. These agents fulfill

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the information collection, analysis and time arrangement in agricultural production. For example, when emergency takes place, task management agent will call emergency treatment agent which gives reaction and information prompt and makes assistant decision on emergency. Task resolving agent. Task resolving agent consists of many agents. When user or other agent sends out task or cooperation requirement, task resolving agent searches and call the relevant agents, then submits task request. If the agent receiving task is idle, it will accept the task and begin to address the task. The results will be returned to task resolving agent by communication and task management agents. These collaboration agents include: Information analysis agent. Which makes out statistical analysis and creates various graphs and report forms. Agricultural product tracing agent. It is responsible for the information collection, filtration, treatment, storage, earmark and bar code creation of the whole supply chain from production to processing to table. Agricultural product traceability agent. Which searches for each tache of the whole supply chain aiming at the different demands of consumer, enterprise and government, for instance whether each tache accords with respective standards or not. Market analysis and forecast agent. It analyses market information and comes into being market forecast with various methods. If there is a great discrepancy between the two forecast results, one result or several results will be provided to predict market dynamic by means of consultation, competition and ratiocination. Logistics management agent. It administers the logistics information and dispatch. Instant event monitoring agent. On the one hand, it deals with the information from interface; on the other hand, it supervises the abnormity with each sector on supply chain, analyzes whether an emergency occurs or not. If system thinks an abnormal event or emergency has taken place, the early-warning will be sent out and communication starts. Emergency treatment agen. It classifies the emergency for example quality safety, quantity safety, abnormal fluctuation of market and so on. Then the corresponding agent will be called to analyze and search the response plan. If there is not response plan, system gives an alarm to ask for human intervention. Otherwise the response plan will be called to deal with the emergency. At the same time, system will give the assistant decision. Data mining agent. It makes data mining and learning on the basis of collecting a large of information. It promotes the intelligence of agent, needs human intervention and certification. 3.3 Communication Language and Standard between Agents In order to guarantee the veracious cooperation between agents we must standardize their communication manner. Communication of multi-agent has been built on the Agent Communication Language (ACL), and follows ACL standard of the Foundation for Intelligent Physical Agents.


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In cooperation process matching conditions include communication action, message parameter and parameter expression and so on. Every agent has special matching format and combination. Only the message instruction suiting the format completely can be recognized by target agent and implemented. 3.4 Cooperation among Multi-agent System Cooperation embodies the sociality of agent, and is a main predominance of multiagent system. In agent-based agricultural product quality control system, we take certain unqualified product as example. After receiving task requirement, interface agent calls communication agent to deliver the task to task management agent. Task resolving agent analyses the massage from task management agent, then sends out news to data management agent in order. Data management agent looks up the data concerned and sends out to emergency treatment agent. Emergency treatment agent runs the relative models to create a suit of assistant decision scheme, and then calls the other agents to consult. Finally interface agent will receive the plan and data used, which maybe is an assistant decision scheme or a warn-earning message. Each agent can be activated at any moment. Many agents can realize consultation and competition. 3.5 System Development and Application We chose Java language to develop system on Windows operation platform in order to guarantee the universality and transferability of multi-agent system. Agents with various function were constructed by means of exploiting principal part and interating softwares. For the function modules such as collection, monitoring and analysis of bee product information, we made best use of former programme to encapsulate, transfer and build the corresponding function agent. The reuse and share of the research materials on our hand can save workload effectively. The functions such as decision-making and emergency response need the cooperation, consultation, communication and competition among agents to reach the practical results. Communication of multi-agent had been built on the TCP/IP protocol. The most popular agent communication language--KQML (Knowledge Query Manipulation Language) had been used to exchange messages among agents so the cooperation and collaboration decision-making was realized.

4 Conclusion This study on agent-based agricultural product quality control method will provide elementary way and information technology tool for building quality control, supervision and traceability system as well as information platform, will reduce the amount of development work greatly, will increase the ability to answer for the quality safety problems, can offer a tool of information supervision, control and coopration for whole supply chain, will help to realize the digitilization and intelligent management for agricultural products. Acknowledgments. This study was supported by the National Natural Science Foundation of China (Grant No. 60972154) and the National Science & Technology Pillar Program (Grant No. 2009BADA9B02).

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References 1. Chen, S.-d., Song, D.-y.: The development and enlightenment of the quality traceability system of livestock in France and Netherlands. China State Farms 2, 37–41 (2007) (in Chinese) 2. Chen, H.-h., Tian, Z.-h.: Comparative Study on Traceability system of agricultural products at home and abroad. Market Modernization, 5–6 (2007) (in Chinese) 3. Regattieri, A., Gamberi, M., Manzini, R.: Traceability of food products: General framework and experimental evidence. Journal of Food Engineering 81, 347–356 (2007) 4. Zheng, F.-t.: The Adjustment from Food Security System to Food Safety System–The Strategic Transfer Faced by Chinese Food Production Systems. Journal of Finance and Economics 2, 71–75 (2003) (in Chinese) 5. Zhang, Y.-j., Liu, N., Yang J.-h.: Study on Building and Promoting the System of China’s Food Security. China Industrial Economy 2, 14–20 (2005) (in Chinese) 6. Ye, Y.-m.: Thinking about the Establishment of China Food Safety Quality Control System. World Standardization and Quality Management 4, 4–6 (2002) (in Chinese) 7. Zhu, Y., Zhao, J., Liu, S., Yue, E.: Design of bee products quality monitoring information service platform. In: IFIP International Federation for Information Processing, Computer and Computing Technologies in Agriculture II, Beijing, pp. 2141–2149 (2008) 8. Li, S.J., Zhu, Y.P., Liu, S.P.: Status quo of quality safety of bee products and construction of traceability management information system 24, 293–297 (2008) (in Chinese) 9. Liu, S.P., Zhu, Y.P., Li, S.J.: Research on Data Acquisition System for Agent-based Bee Product Traceability Platform. In: IEEE Proceedings of the, International Conference on Computer and Computing Technology Applications in Agriculture (2009) (in Press) 10. Delicious Group Co., Ltd. Shandong.: Analysis of RFID technology in meat and food safety in the application of retrospective. China AUTO-ID, 1, 69–71 (2008) (in Chinese) 11. Wen, X.-y.: The Situation and Development of Food Traceability. Radio Frequency Identification Technologies and Applications 4, 12 (2006) (in Chinese) 12. Chen, J.-P.: China’s agricultural products quality and safety of the status quo and recommendations. unpublished (in Chinese) 13. Liu, J.-h., Dou, Y.-p.: Application of Automatic Identification Technology in Quality Traceability. Computer Simulation 2, 225–227 (2005) (in Chinese) 14. Liu, Z.-z.: Application of RFID in Securing Food Supply Chain. Logistics Technology 6, 18–20 (2006) (in Chinese) 15. Zheng, D.-y., Wei, Q.-b., Feng, J.-y.: Implementation of Tracing and Tracking Safety Mechanism of Agricultural Product Packaging Based on RFID. Packing Engineering 27, 153–158 (2006) (in Chinese) 16. Giordano, R.: An intelligent agent for the groundwater pollution risk evaluation. In: Coastal Zone Management in the Mediterranean Region, Valenzano, Italy, pp. 47–56. CIHEAM-IAMB (2002) 17. Lima, R.M., Sousa, R.M., Martins, P.J.F.: Distributed production planning and control agent-based system. International Journal of Production Research 44, 3693–3709 (2006) 18. Patt, A., Siebenhüner, B.: Agent Based Modeling and Adaptation to Climate Change. Vierteljahrshefte zur Wirtschaftsforschung 74, 310–320 (2005) 19. Roche, C., Fitouri, S., Glardon, R., Pouly, M.: The Potential of Multi-Agent Systems in Virtual Manufacturing Enterprises. In: 9th International Workshop on Database and Expert Systems Applications, DEXA 1998 (1998) 20. Yang, G.-w., Du, Y.-f., Guo, L.: Research of Multi-Agent System. Computer Knowledge and Technology 4, 322–323 (2008) (in Chinese)


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21. Ying, L.: Studies of Agent-based Project Management Information System. Journal of East China University of Science and Technology, Social Science Edition 1, 61–64 (2007) (in Chinese) 22. Wang, N.-h., Wang, C.-l.: Application Research on Mobile Agent in Expert System of Forest Diseases and Pests. Forest Engineering 3, 10–13 (2008) (in Chinese) 23. Zhao, L.-d.: Execution and Evaluation Strategies of Multi-agent Diagnostic Task. Journal of Industrial Engineering and Engineering Management 2, 86–89 (2003) (in Chinese) 24. Zeng, Y.-c.: The Summary of Agent Technology Researching. Computer Knowledge and Technology 21, 525–527 (2008) (in Chinese) 25. Quan, L.-n., Fan, L.-p.: Agent Technology Application on Water Quality Decision Support System. Machinery & Electronics 5, 77–79 (2006) (in Chinese) 26. Liu, H., Wang, H., Wang, W.S.: Study about MAS-based Agricultural Expert System Platform. Microcomputer Development 13, 8–10 (2003) (in Chinese) 27. Zhu, Y.P., Li, S.J., Liu, S.P., Xu, Y.C.: Agent-based cooperative analysis and decision support system for regional agricultural economic information. In: Arabnia, H.R., de la Fuente, D., Jose, A. (eds.) Proceedings of the 2009 International Conference on Artificial Intelligence, WORLDCOM 2009, vol. 1, pp. 96–99. CSREA Press, Olivas (2009) 28. Xue, L., Zhu, Y.P., Xue, Y.: Rearch on agriculture economy intelligent decision support system based on multi-agent system. System Sciences and Comprehensive Studies in Agriculture 20, 172–176 (2004) (in Chinese) 29. Zhu, Y.P., Li, S.J., Liu, S.P., Xu, Y.C.: Agent-based cooperative analysis and assistant decision-making method for regional agricultural economic information. In: Sixth International Conference on fuzzy Systems and Knowledge Discovery, vol. 7, pp. 317–321 (2009) 30. Liu, S.P., Zhu, Y.P.: Establishment of wheat simulation model based on agent technology. Journal of Anhui Agricultural Sciences 35, 6026–6028 (2007) (in Chinese) 31. Fan X.-f., Zhou D.-y.: Information traceability and food safety management of agricultural products. Commercial Times 12, 87–88 (2007) (in Chinese) 32. Wu, H.-j., Ni, W.-h.: Design of Traceability Architecture of Food Supply Chain. Industrial Engineering Journal 5, 45–65 (2008) 33. Zhang, W.-b., Gu, Z.-y.: Mechanism analysis on food safety based on management of food supply chain. Science and Technology of Food Industry 28, 215–220 (2007) (in Chinese) 34. Fang, Y., Gao, G., Fan, X., Chen, H.-n.: Research on China’s food safety tracing system. Agricultural Quality and Standards 2, 37–39 (2005) (in Chinese) 35. Wang L.-f., Lu C.-h., Hu Y.-n., Xie J.-f., Bai H.-w., Bai Y.-f.: Design and implementation of a novel swine tag and traceability system for swine and meat products. Agriculture Network Information, 17, 25–27 (2006) (in Chinese) 36. National Bureau of Statistics Of China.: 2008 National Economic and Social Development Statistical Communique (2008) (in Chinese) 37. Wu, D., Lu, C.-Y., Chen, H.: Food quality and safety traceability system research progress and application prospect in the tea industry. Chinese Agricultural Science Bulletin 1, 251– 255 (2009) (in Chinese) 38. Tykhonov, D., Jonker, C., Meijer, S., Verwaart, T.: Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks. Journal of Artificial Societies and Social Simulation 11(3, 1) (2008) 39. Mangina, E., Giavasis, I.: The theoretical framework of agent based monitoring for use in the production of gellan gum in a microbial fermentation system. International Journal of Computer Science & Applications, Techno-mathematics Research Foundation 2, 38–48 (2005)

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40. Moises, R.-F.: A Principal-Agent Model for Investigating Traceability Systems Incentives on Food Safety. In: 105th EAAE Seminar, International Marketing and International Trade of Quality Food Products, pp. 127–138 (2007) 41. Zhu, L., Wang, H.-y.: Research on Quality Control System of Food Supply Based on Chain Supermarket. Logistics Technology 10, 206–208 (2005) (in Chinese ) 42. Deng, N., Liu, J.s., Luo, R.-s.: Agent- based Research on Supply Chain Risk Management. Chinese Journal of Management Science 10, 412–416 (2006) (in Chinese) 43. Xiao, Y., Liang, G.-q.: Quality supervise mechanism of supply chain model base on MultiAgent system. Modern Manufacturing Engineering 2, 26–28 (2007) (in Chinese) 44. Li, F.: Goods Tracing System Based on RFID and Agent Technology. Computer Engineering 34, 277–279 (2008) (in Chinese)

Design of ETL Process on Spatio-temporal Data and Study of Quality Control* Buyu Wang1, Changyou Li1, Xueliang Fu1, Meian Li1, Dongqing Wang1, Huibin Du1, and Yajuan Xing2 2

1 Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China Inner Mongolia Fengzhou Vocational College, Hohhot, 010018, P.R. China [email protected]

Abstract. In order to use the space-time data mining technology to conduct operation research in WuLiangSuHai Eutrophication, the water quality sensor parameters of heterogeneous data which reflect the characteristics should set up a spatial data warehouse through ETL process, and water quality sensors for quality control of spatial and temporal data plays a vital role in building an effective analytical environment. The paper designs the ETL process from the data and water quality sensors artificial duty and other heterogeneous data sources spatial data, and proposes data quality control strategy based on the incremental frequency rule engine and the space the inverse distance weighting on the Combination. Experiments show that the incremental frequency rule engine could more effectively find the missing sensor data and abnormal, Space inverse distance weighting method can find the missing data and outliers in the errors within the allowed interpolation processing, ETL procedure is effective and feasible. Keywords: Sensor data, Frequency increment rule engine, Inverse distance weighted method.

1 Introduction At present, the integrated analysis and process of water quality services have been investigated using the spatial data mining techniques [1], [2], [3]. In particular, some work has studied multi-eutrophication services of water environments based on the heterogeneous data sources [4]. The pre-requisite for the investigation of eutrophication related services is to establish a data warehouse including the water-quality data characterized by a variety of eutrophication attributes. The design of the ETL (Extract, Transform, Load) procedure plays a key role to establish such an effective analysis environment. ETL process design, many scholars from the conceptual model, conceptual model to logical model of the transformation of both done a lot of research work [5], [6]. *

The research is supported by Chinese Natural Science Foundations (50969005,40901262) and by Specialized Research fund of High Education for Inner Mongolia (Njzy08046).


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Abnormal data, some scholars have proposed the use of rules engine to detect, and made the related theoretical research work, but did not give a specific implementation strategy [7]. Deal with the problem of missing data values, some academics have suggested the use of inverse interpolation method, the weight-related research and in the field of meteorology has been successfully applied [8]. In this paper, three aspects of the work done. Designed with the characteristics of lake water quality data ETL process. A comprehensive consideration of water quality parameters of water eutrophication factor weighting inverse method interpolation. Designed based on the Drools rule engine kernel dynamic incremental realized abnormal test data volume.

2 Design of the ETL Process with Heterogeneous Data Sources 2.1

Heterogeneous Data Sources

In order to study the issues related to the water eutrophication, data source, on which the analytic environment is established, should consist of two kinds of data: the data of water quality eutrophication and the data representing the key elements of eutrophication. In this paper, the data source mainly includes the following two parts: the water-environment monitoring data and the data manually collected in the Wuliangsuhai Lake. The water-environment monitoring data is collected by the on-line waterquality sensors in the Wuliangsuhai wireless sensor network, and the time granularity of the data sampling is once per 15 minutes, which is conducted in each monitoring station in the Wuliangsuhai Lake. The manual collected data comes from the researchers who collect the related data on the spot in summer, and the associated time granularity is once per day, the sampling space varies each day. Obviously, the data is inconsistent with the manually collected data in terms of time granularity and space granularity. 2.2

Design of the ETL Process

The ETL process provides data for data warehouse. Therefore, the quality of the ETL process relates to the success or failure of the establishment of data warehouse. The ETL processes adopt different strategies and implementation methods for dealing with different data sources. This paper proposed the design of the ETL process which can unify the heterogeneous data sources varying with time granularity and space granularity. The proposed ETL process is as follows. Firstly, the cleaning of the heterogeneous data is performed with the strategy of data quality controlling introduced next. Secondly, the data uniformity of space granularity is achieved as follows: extract the monitoring data of the sensors located in the sampling stations which are near to the man-made sampling points in terms of latitude and longitude. Following this, the data uniformity of time granularity is achieved as follows: superimposing and fitting of 96 sensor groups of data of 96 sampling times over 24 hours. Thereby, the transformation of data is completed. Finally, the consistent data is loaded in the data warehouse.

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3 Data Quality Control 3.1

The Missing or the Abnormal of Water Quality Data

The issues related to the data quality of water-quality sensors mainly are manifested in the two following aspects. One aspect is that WSN is affected by the quality of the communication signal in the data acquisition and transmission. As a result, the sampling data may be missing in some time intervals. The other is that the electrical signal noise and man-made factors may lead to the sampling data abnormal during the process of sensor monitoring. This paper investigates how to deal with the data missing and the data abnormal, which is essential for the control on data quality in the ETL process. 3.2

IWQPW Interpolation

For k points of sensor data in some sampling period employed in the procedure of the data uniformity, the missing and abnormal data may result not only from the parameters of water-quality sensors, but also from the signal strength which may varies with the sampling frequency over one hour. This paper took into account the signal strength and the factors of time period and sampling frequency, and proposed the IWQPW (Inverse Water Quality Parameter Weighting) interpolation method to cope with the issues mentioned above. Definition 1. Assume that the starting instant is t0, and a group of data is manually collected data over 24 hour period. Accordingly, 96 groups of water-quality sensor data have also been collected, which are indexed by a sequence of integer numbers. Each index corresponds to the related sampling instant. Definition 2. Assume that the data record is a group of manually collected data, the 96 corresponding sequential groups of data is RD = {d1 , d 2 , …, d 96 } . For any d i (1 ≤ i ≤ 96) and d j (1 ≤ j ≤ 96 and i ≠ j ) in RD, if their indices are x and y respec-

tively, the sequential distance between two sampling point is calculated as x − y . Definition 3. Let M (x, y, z, t) be an arbitrary interpolation point in the sample space, N (xi, yi, zi, ti) be an arbitrary point in 96 sequential sampling values. The sequential weight of the sampling point N, Wi, t is defined as:

wit =

1 l − lk

(1 ≤ k ≤ 96)

(1)

Where l indicates the sequential position of the interpolation point, lk indicates the sequential position of the sample point. Definition 4. IWQPW (Inverse Water Quality Parameter Weighting) is a spatial temporal sequence interpolation method with the comprehensive consideration of the weight of water quality parameters and the sequential weight. IWQPW takes the distance as weight between the interpolation point and the midpoint of the sample

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space for weighted average calculation. The sample point is assigned a larger weight if it is nearer to the interpolation point and with a short sequential distance. Assume that a monitoring station is a basis. There are n samples in the 96 sequential sensor sampling space. Let zi be the value collected of water quality, z be the value of water quality to be estimated as follows: n

n

i =1

i =1

z = ∑ ( zi × wi ) / ∑ wi 3.3

(2)

Dynamic Incremental Rule Engine

3.3.1 Dynamic Incremental Rule Engine Overview The rule-based engine technology originates from the rule-based expert system. It has been becoming a popular research topic recently that the application of this technology to the ETL process for the control of data quality. Due to the hidden of the abnormal data collected by water quality sensors, it is not easy for non-professionals to tell the abnormal data. This paper proposed to exploit the rule-based engine technology to deal with the issue that how to make the outlier detection rules dynamic increment with the accumulation of expert experiences and the development of related disciplines. However, the following two issues appeared in the application of this technology to the detection of outlier in the sensor spatial data. • Existing data-cleaning methods based on the rule engine can only process a data record. However, the sensor parameter data is closely related to the data from time and space. Thus, one record data is not sufficient to identify the outliers. Therefore, it is necessary to consider all these related data together. • It is not flexible of existing cleaning methods based on the rule engine to process the rule file. The historical versions of the domain-expert rule files cannot be made full use. Especially, it is impossible for the existing rules to self-learn and update continuously.

This paper investigated the method for the detection of outliers in the sensor parameter data based on the rule engine technology in order to cope with the above two issues, and proposed a new engine technology of dynamic incremental rules. 3.3.2 The Architecture of the Dynamic Incremental Rule Engine The designed dynamic incremental rule engine is Drools rules engine as the rules of the nuclear, through plug-in components, to achieve the continuous dynamic growth rule and incremental updates. The architecture of the dynamic incremental rule engine includes: Rule generation interface. Graphical user interface (GUI) is exploited to edit the user rules and the plan of the rule conversion. In GUI, the rule is represented using custom XML files. Rule-related job dispatcher. The dispatcher is used to dynamically adjust the priority of the rules and the sequence of the rule conversion.


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Rule converter. The rule converter takes the responsibility of the conversion of the custom XML rules to the rules supported by Drools. That is, the converter transfers the object-oriented rules to the Drools-supported rules. Rule-version controller. With the rule-version controller, the user rules are managed in a centralized manner. The rules with old versions are also regulated. The system maintains a record list regarding the rule usage of each individual user with specific role(s). The personalized interface thus is provided to different users. Runtime database. The database is used for data persistent storage, which is shared by the other components. Code generator. The generator produces the code according to the rules, and returns the data anomalies stamp. There are two kinds of return data. One is the PL/SQL code; the other is Java code. The generated code, as input, sends to the module of data layer. Data layer modules. Data layer modules receive the code, and determine which operation should be performed according to the abnormal stamp. If it is necessary, the interpolation module is called. Then, the persistence operation is performed. 3.3.3 A Sample of the Rule File This dynamic incremental rule engine designed to use a custom XML file to represent the objects of the rules, call the Java API code embedded in the rules file, rules through five steps to achieve the dynamic and incremental update capability, in detail Process described below. • Import the classes used in the code, nested the classes in <java:import> tag; • Describe data set schema. The original data set is divided into the current data set and the other data set, differentiated by the attribute tag, i.e., type. The sample fragment is as follows for the definition of the data set. <java:receive name='sensorDataset'> <java:ds name='currentdataset' type='current'> <parameter name='sensor'> sensorDataset <parameter name='struct_sensor' type='srcstruct'> <matched column value='time'> … • Define the function of object operators. The operations are defined between the objects in the set of abstract source data and the objects in the set of training data.

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The specific function definition is included in the <java:functions> tag. The sample fragment is as follows: <java:functions> public int positonInOtherDataset(currentdataset, otherdataset, itemtocompare) { //Return the position of the specified field of the //current data set in the other data set ordered by //the related values } • Define the specific rules. The Boolean expressions are used to describe the rule conditions, which consist of the class, the data sets and the operator functions defined in the above, the sample fragments is as follows: <java:condition name1='cond1' cleanitem='phx'> isMaxValue (currentdataset, otherdataset, "phx") equals the value of a return code • Define an operation. The described operations are performed when the Boolean expression of the rule condition is true.The sample fragment is as follows: <java:consequence> markExceptionData (data sets,outlier row,outlier column); 3.4 Quality Control Strategy

The process of data cleaning is the most critical part of quality control of waterquality-sensor data, which includes two parts: the interpolation processing of the missing data in water quality monitoring and the detection processing of the outlier. First of all, the missing values of sensor water-quality data are interpolated using IWQPW method. Then, the outlier detection is run using the dynamic incremental rule engine with the input of the processed data set and the rules edited by water experts. Following this, the space interpolation is performed on the outliers using IWQPW method again. Finally, the achieve data set get into the follow-up processing.

4 Experimental Design and Analysis The purpose of the experiment is to verify the accuracy and reliability of IWQPW interpolation, as well as recall ratio and precision ratio of DIRE rule engine. Recall ratio is calculated as the ratio of the number of detected outliers over the number of


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statistical outlier. Precision ratio is defined as the ratio of the correct number of detected outliers over the number of detected outliers. The test set A consists of the four sampling spaces indexed by 1, 2, 3 and 4 respectively. Each sampling space is constructed based on the test sampling space of the real data records of PH, ORP and oxygen content collected in April in the Wuliangsuhai Lake. Then, the test set B is obtained through elimination of the uncompleted record and outliers in the four sampling spaces in the set A under the guidance of related experts. Experiment 1. Under the guidance of related experts, the statistical number of outliers is achieved manually for each sampling space in the test set A. The number of outliers is also obtained from the DIRE rule engine with the input of each sampling space. Thereby, recall ratio and precision ratio can be calculated. Experimental results are shown in Table 1. Table 1. Detection of outliers in sensor data with the rule engine No.

Number outliers

Number detected

Correct number

Recall ratio

Precision ratio

1 2 3 4 5

364 253 377 423 340

345 237 344 389 309

337 229 319 377 298

94.7% 93.7% 91.2% 92.0% 90.9%

97.7% 96.6% 92.7% 96.9% 96.4%

Experiment 2. The purpose of this experiment is to compare the ratio of interpolation accuracy using three following methods. From the test set B, we remove normal data 4 times with the quantities of 105, 150, 245 and 400, respectively. Then, three interpolation methods, i.e., IDW, Kriging and IWQPW are used individually for data interpolation within the allowable range of the error (0.62). The experiment results are shown in Table 2. Table 2. Accuracy of different interpolation methods for sensor data Measuring the number of missing values 105 150 245 400

Interpolation of the average accuracy IDW Kriging IWQPW 79.2% 77.7% 99.6% 77.3% 78.2% 96.2% 66.0% 66.9% 95.4% 70.3% 70.5% 90.0%

The experiment results show to some extent that our proposed method is useful in practice and effective. The results also show that our methods strongly rely on the rules.

5 Conclusion The objective of this paper is to deal with the issues related to the quality control of water-quality sensor data. The ETL process is first proposed in order to establish data

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warehouse. In addition, the quality control strategy is proposed which combines IWQPW method with the dynamic incremental rule engine. Thereby, it can be clean up that the missing values and outliers. The future work is to investigate further universal interpolation methods and the self-learning capability of the rule engine.

References 1.

2.

3.

4. 5.

6. 7. 8.

Chen, Q., Mynett, A.E.: Integration of data mining techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake. J. Ecological Modelling 162, 55– 67 (2003) Lenat, D.R.: Water Quality Assessment of Streams Using a Qualitative Collection Method for Benthic Macroinvertebrates. J. Journal of the North American Benthological Society 7, 222–233 (1998) Neal, C., Robson, A.J.: A summary of river water quality data collected within the LandOcean Interaction Study: Core data for eastern UK rivers draining to the North Sea. J. Science of the Total Environmen. 251, 585–665 (2000) Codd, G.A.: Cyanobacterial toxins, the perception of water quality, and the prioritisation of eutrophication control. J. Ecological Engineering 16, 51–60 (2000) Vassiliadis, P., Simitsis, A., Skiadopoulos, S.: Conceptual modeling for ETL processes. In: 5th ACM International Workshop on Data Warehousing and OLAP, pp. 14–21. ACM, New York (2002) Simitsis, A.: Mapping conceptual to logical models for ETL processes. In: 8th ACM International Workshop on Data Warehousing and OLAP, pp. 67–76. ACM, New York (2005) Loshin, D.: Rule-based data quality. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 614–616. ACM, New York (2002) Sun, Y., Kang, S., Li, F., Zhang, L.: Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China. J. Environmental Modeling & Software 24, 1163–1170 (2009)

Design of Fuzzy Drip Irrigation Control System Based on ZigBee Wireless Sensor Network Xinjian Xiang College of Automation & Electrical Engineering, Zhejiang University of Science and Technology, Zhejiang, P.R. China [email protected]

Abstract. To improve agricultural water resources’ utilization, crop’s automatic, locate, time and appropriate drip irrigation is a good choice. In this paper, an automatic control drip irrigation system based on ZigBee wireless sensor network and fuzzy control would be introduced. System uses CC2430 for wireless sensor network node design, collecting soil moisture, temperature and light intensity information and sending the drip irrigation instructions by the wireless network. System put this three soil factors input fuzzy controller, created fuzzy control rule base and finished crop irrigation time fuzzy control. This paper mainly describes system’s hardware structure, software design and working process. The system with the characteristics of economical, reliable communications and high accuracy control, could improve agricultural drip irrigation water using efficiency and the automation level. Keywords: Drip irrigation, ZigBee wireless sensor network, Fuzzy controller, Drip irrigation automation.

1 Introduction Agricultural water low use efficiency, shortage and waste are big problem of currently development of irrigated agriculture. Drought is the major environmental stress factors for crop growth, which is more than all other factors’ sum up[1]. Drip irrigation is a system that directly supply filtered water, fertilizer or other chemical agents to soil with slow and regular drip through the trunk, branch and capillary on the emitter under the low-pressure. It’s utilization of water could up to 95%, Drip irrigation is an important technology in irrigated agriculture and the ideal solution to resolve the effects of drought. Over the years, most of our drip irrigation system controlled by manually experience without real-time data collection and analyze, drip of arbitrary is large. Thereby, study of automatic drip irrigation system has a great significance. Implementation of irrigation automation requires as following [2-3]: 1) the accurate collection of crop water requirement; 2) the remote information transmission technology for water demand information and the control; 3) drip irrigation control decision-making. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 495–501, 2011. © IFIP International Federation for Information Processing 2011

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Many researches carried out at home and abroad. However, there is still clearly insufficient with 2) and 3) for application:1) Currently most of drip irrigation control systems work with serial bus or field bus technology, the wiring inconvenience and high cost, longer time-consuming make it is hard to promote in practice [4]. 2) Automatic drip irrigation as a complicated system, Irrigation decision-making affected by soil, crops and the environment’s multi-sensor information. There is still a lack of appropriate control strategies [5]. In recent years, with the development of wireless information transfer technology, ZigBee wireless network with its lowpower, low cost, low rate, close, short latency, high-security features get attention in agricultural production. Scholars begin to study the drip irrigation system with wireless technology. However, in these drip irrigation systems, ZigBee wireless sensor network is mainly used for collecting soil, crop or environmental information, providing drip irrigation decision-making. Information collection and automatic irrigation control integrated system based on ZigBee technology is rarely. Fuzzy control for the many complex and difficult to establish accurate mathematical model system control provides a solution, it’s study about drip irrigation only consider the soil moisture information as fuzzy inputs, neglected crops and environmental information, which cause the decision-making is not accurate enough. To this end, a design of fuzzy drip irrigation control system based on ZigBee wireless sensor network is provided. The system consists of low-power wireless sensor network node with self-composed ZigBee network formation, avoiding the inconvenience of wiring and poor flexibility shortcoming, achieving continuous online monitoring of soil moisture. System uses soil moisture, temperature and light intensity information for fuzzy decision-making, and completes the fuzzy control of drip irrigation automation. It would improve irrigation water use efficiency, ease the growing tension of water resources conflicts and provide a good growing environment for the crop.

2 System Hardware Design 2.1 System Composition System based on ZigBee wireless network, is made up of drip irrigation system, ZigBee wireless network nodes and monitoring center. Due to real-time monitoring information of soil moisture, temperature, light intensity and the crop water use law, implement of automatic drip irrigation with fuzzy control strategy, as shown in Figure 1. 2.2 Drip Irrigation System Design For drip irrigation requirements, throttle, filters, and pressure gauge should be installed at the water source. System use PVC Ф32 mm for main pipe, PE Ф20 mm for branch, with pressure compensation emitter, one plant with a drip emitter embedded in the branch. Front-end of branch connected solenoid valve with 24 V DC, flow rate of 2.3L / h, and pressure gauge. Branch spacing can not be too small for preventing interference between lines caused by water infiltration, and initial set line spacing to 1 m. Soil moisture sensor buried under the roots of the plant near the surface, light intensity sensors and temperature sensors fixed to the side of the pole

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on the plant. Sensor signals input CC2430 to constitute the measurement of soil moisture ZigBee wireless sensor network node. Each solenoid valve coupled to the CC2430 ZigBee module circuit, composed of drip irrigation control wireless sensor network node. 2.3 ZigBee Wireless Network Node Design ZigBee wireless sensor network using star network topology. Node is divided into three categories: sensor node, controller node and routing node. In the design, three kinds of nodes all use TI’s CC2430 as a common core module, and different expansion modules, as shown in Figure 2. CC2430 with strong function and rich on-chip resource, only need few external components can be achieved with the signal transceiver functions, which made the hardware design for three kinds of nodes are very simple, reliable and practical. 2.3.1 Design of Zigbee Wireless Sensor Network Node for Soil Moisture Measurement Sensor nodes connected with the soil moisture sensors is used to read and transfer sensor information. Soil moisture sensor nodes’ spatial arrangement will be optimized according to crop type, soil type, terrain conditions and reliable signal transmission requirements. It includes Soil moisture sensors STHO01, digital temperature sensor DS1802B and photosensitive resistance P9003. STHO01 soil moisture sensor measurement accuracy of ± 3%, range 0 to 100%, output signal 4 ~ 20mA, operating voltage 12V DC, stabilization time after power 2 s, can meet the requirements of realtime monitoring. The output signal change to 0 ~ 5 V voltage through the highprecision resistor, then converted into digital signal by the CC2430 AD module, soil moisture can be determined from different voltage amplitude. STHO01 should be buried in the ground, the location and drip irrigation start time is close to the data accuracy and time. General crop root depth of 10 ~ 20 cm, Drip Irrigation humid time of 5 min-30 min, thus burying depth of the sensor is set to 15CM, open time is set to 20min after drip irrigation. Signal reception and transmission by the antenna. Each sensor node is powered by solar cells, and the battery voltage is monitored at any time, once the voltage is too low, the node will send a low voltage alarm signal, then the node run into sleep mode until it is fully charged. 2.3.2 Design of ZigBee Wireless Network Drip Control Node and Routing Node Control node is connected with the irrigation control panel to control the open/close head of drip irrigation and valve through Timer based on fuzzy control strategy. In addition, the control node has the interrupt response capability to deal with control commands from the computer. As the irrigation control panel and electric control valves use electricity supply, so does the control node. Between the core module and the irrigation control panel using optocouplers in order to avoid strong electrical interference. System’s routing nodes create a multi-hop network in self-formation. Sensor nodes distributed in the monitoring area, sent the collected data to the wireless routing node nearby, then routing node selects the best route according to the routing algorithm to establish the appropriate routing list. Routing node connect with base station for address allocation, management, monitoring, signal transmission and

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reception between the sensor node and control node. The routing node sends a data read command to sensor node every 20 min, and upload the receive data through the serial port to the base station computer.

Fig. 1. System diagram

Fig. 2. Structure of wireless sensor network node

3 System Software Design 3.1 Fuzzy Control Strategy Design The crop’s water requirement is related to soil moisture index, meteorological conditions (radiation, temperature, etc.), crop type and growth stage. Therefore, the system chooses soil moisture, temperature and light intensity as the fuzzy controller input. Fuzzy controller input for soil moisture (WH), temperature (WT) and


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light intensity (WL), the output for the irrigation time (WT), as shown in Figure 3. In order to ensure appropriate accuracy, four variables are defined five linguistic variables: very light (VL), light (L), middle (M), heavy (H), very heavy (VH). In the choice of membership function (MF), triangular MF is simple, computationally efficient, especially for applications that require real-time implementation of the occasion, so the system using triangular MF fuzzy: translate the variable’s exact value into fuzzy linguistic variable value in the appropriate domain, that determine input/output range and the domain of fuzzy linguistic variables. Fuzzy Reasoning: knowledge-based reasoning by a certain mechanism, get the fuzzy output value from the fuzzy input. Inference rule is summarize by experience get "IF-THEN" statements express, such as experience, when the soil moisture below the lower limit, indicated that the soil is extremely dry at this time regardless of the level of other inputs, crops need a lot of irrigation, written in fuzzy reasoning Rules that "ifWT is VL thenWT is VH". In practice, different situations also need to adjust the rules, and gradually create the best irrigation scheme. Ambiguity: According to the results of fuzzy reasoning by multiplying the scale factor, get the exact output amount needed to control the system. In this system, the center of mass defuzzification method is used to obtain the irrigation control valve opening time. Soil potential

water

Fuzzy controller

Fuzzy Reasoning

Irrigation output

Farmland evapotranspiration Fuzzy control rule base

Fig. 3. Structural principle of fuzzy controller

3.2 Node Software Design In the irrigation control system, monitoring data and control commands are transmit in the wireless sensor nodes, wireless control node, the wireless routing nodes and the monitoring center. Sensor nodes and control nodes turn on the power, initialization, and get in sleep after the establishment of links. When the routing node receives an interrupt request, activate the sensor nodes and control nodes, send or receive packets, continue into hibernation after processing, waiting for a request to activate again. In the same channel, only two nodes can communicate through the competition to get the channel. Each node periodically in sleep and monitor mode, taking the initiative to seize the channel when the channel is idle, and retreat for some time based on backoff algorithm to re-monitor channel state when the channel is busy. In the programming design, system mainly uses interrupt method to complete send and receive message.

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X. Xiang

3.3 PC Monitoring Software Design Monitoring software plays a vital role in this system, written using VC #, through the monitoring software to achieve the ZigBee network monitoring, information extraction, fuzzy control calculation and control output functions. First, the software shows the topology of wireless networks, after confirmation system begin to receive node sensor signal in scheduled, the signal can be displayed in two ways: numerical display and curve display, collection steps can be set to 20min, then finish fuzzy control calculation according to fuzzy control method, output control node signal and control the electromagnetic valve’s switching time. The sensor signals and output control signals can be timed automatically saved and exported to the interface for observation and comparison.

4 Application and Validation System’s initial test is in the vineyard’s drip irrigation. The vineyard uses fixed ground drip irrigation system, each block with a main pipe and some branch comb pipes, electric control valves installed at the end of the main channel, each electronic control valve connect with a wireless sensor network controller node which control the block’s drip time. In the experiment, four blocks are selected, around the block controller node distance 70 ~ 120 m, while the sensor nodes are distributed in an approximate square area around the block (each block containing 1 to 3 sensor nodes), while the base station (router nodes) farthest away from the node 250 m. Experiments show that nodes with distance of 200m, the single communication error rate is less than 2%. System using repeated comprehensive judgments to improve the reliability of communication. In addition, the electronic valve control accuracy, and system run in good condition overall.

5 Conclusions In this paper, an automatic control drip irrigation system based on ZigBee wireless sensor network and fuzzy control had been proposed. System uses high-precision soil moisture, temperature and light sensors with low-cost, low power ZigBee wireless communication technology to monitor soil moisture on line, fuzzy control implementation of soil moisture and crop water use rules which are difficult to establish accurate mathematical model for drip irrigation automation. The design avoids the inconvenience of wiring, and improves the flexibility and maneuverability of watersaving drip irrigation control system. Not only can effectively solve the agricultural irrigation water use, ease the growing tension of water resources conflicts, but also provide a better growing environment for the crop, give full play to the role of the existing water-saving devices, optimal scheduling, improve efficiency, so drip irrigation is more scientific, convenient, enhance the management level. The system also supports remote setting of parameters and control for a variety of crops, can increase crop yield, reduce the cost of agricultural drip irrigation, improve the drip irrigation quality, has great value in applications.


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Acknowledgement This material is based upon work funded by Zhejiang Provincial Natural Science Foundation of China under Grant No. Y108268.

References [1]

[2] [3] [4] [5]

Fang, X., Zhou, Y., Cheng, W.: The design of ireless intelligent irrigation system based on ZigBee technology. Journal of Agricultural Mechanization Research (1), 114–118 (2009) Xie, S., Li, X.: Design and implementation of fuzzy control for irrigating system with PLC. Transactions of the CASE 23(6), 208–211 (2007) Jin, Z., Xu, M., Wei, X.: Study and design of spraying irrigation automatic controller based on fuzzy decision. Drainge and Irrigation Machinery 22(5), 26–28 (2004) Jiang, M., Chen, Q., Yan, X.: Precision irrigation system based on fuzzy control. Transactions of the CASE 21(10), 17–20 (2005) Yang, X.: Research on power consumption in sensor network. In: Microcontrollers & Embedded Systems, vol. (1), pp. 27–29 (2006)

Design of Greenhouse Environmental Parameters Prediction System Haokun Zhang and Heru Xue College of computer & information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, P.R. China [email protected]

Abstract. This paper designs an environmental parameters prediction system based on web for greenhouse. The system is designed using the MVC framework, and includes monitoring module and prediction module. The system can obtain the main environmental parameters from sensors, such as light, temperature, humidity, CO2 and so on. Based on mass and energy balance principle, the prediction module of the system can predict the parameters of the greenhouse environment each day. The system displays the measured real-time data and the predicted data for the users to manage greenhouse easily. This paper provides a specific method to realize an intelligent management system for greenhouse. Keywords: Greenhouse environmental prediction, Prediction model, MVC framework, Intelligent management system.

1 Introduction Solar greenhouse is a unique greenhouse structure in China, with low cost, low running cost, good insulation and high efficiency advantages. But the current level of greenhouse environmental control is lower, and the greenhouse environmental control is still a manual control-oriented. It is difficult to adjust to the best environment for crop growth. This paper designs an environmental parameters prediction system realizing a function of remote monitoring and early warning. The system provides reliable and accurate greenhouse environmental parameters for users to manage the greenhouse. With the development of the Internet and WWW technology, Web has become the interactive interface for most software users. WWW is considered the most successful information system. In particular the development of dynamic Web technologies having come a long way, WWW is becoming the mainstream of various types of information system development platform. Dynamic Web system structure is a three-tier client/server model. In the three-tier system architecture, Web browser occupies client layer, database server and other external service account the service layer, and occupy the middle layer is the Web server and server extensions. Three-tier structure makes D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 502–507, 2011. © IFIP International Federation for Information Processing 2011

Design of Greenhouse Environmental Parameters Prediction System

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the dynamic Web browser users can access the existing database resources, and enhances the system interactivity. This paper designs a web-based system, which implements the B/S design pattern and three-tier structure to shield underlying network and provides the users a friendly and consistent interface.

2 System Designing The environmental parameters prediction system is based on B/S design pattern of the dynamic three-tier architecture of Web systems[1]. The users request to the server by submitting a form in a browser. The server calls the data in the database after receiving the requests, and the results are returned to the users. Using software engineering, the system is divided into different functional modules, according to the setting of the types of the greenhouse environmental parameters and the processes and characteristics of greenhouse environmental parameters monitoring and prediction. The system implements functions of monitoring and predicting the greenhouse environmental parameters and provides an interactive platform for users. The structure of system is shown as fig. 1.

Fig. 1. System flow chart

The system includes the following functions: Function of login and registration. Before using this system, users need to register and login. After users submit the registration information successfully, the system returns the registration information to the users. This function is designed to manage the system for users. Function of environment monitoring. This function implements transmission and display of the environmental parameters and stores the parameters in the database. The system sends the parameter data to the users’ browsers using the web server. The function can show the environmental parameter data in a table and also can draw a line chart of the data to users. Function of environment prediction. The implement of this function is base on solar greenhouse environment model, which uses mass and energy balance equations to

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describe the climate in the greenhouse. The function can predict the greenhouse environmental parameters everyday. Input parameters need by this function submitted, the prediction is calculated based on environmental prediction model, and the prediction results is displayed in a table or a line chart to users.

3 System Implementation In the development of the system, JSP technology and DAO technology are used. JSP technology is based on Java, and can create dynamic Web pages supporting crossplatform and cross-server. Following the object-oriented design, JSP programming is easy and independent of web browsers[2-3]. In developing web information systems, JSP technology is widely used. 3.1 MVC Architecture MVC is a "Model-View-Controller" in the abbreviation. MVC applications always have three parts, which are Model, View and Controller. Event leads to changes coursed by Controller in Model or View, or changes both at the same time. If Controller changes the data or properties of Models, all Views automatically update. Similarly, if Controller changes the View, the View gets data from the Model to refresh itself. 3.2 Access to Database This system is designed to use Access desktop database. All operations on access to database are packaged in a separated Java class named by DB.java, in which all member functions are defined as static functions, such as Connection getConn(), getStatement(Connection conn), getResultSet(Statementstmt, String sql) and so on. The following statement can implement the access to database: Class.forName("sun.jdbc.odbc.JdbcOdbcDriver"); conn = DriverManager.getConnection("jdbc:odbc: driver={Microsoft Access Driver (*.mdb)};DBQ=path"); The “path” in the above sentence is the variable of the physical path of the data file. 3.3 Implementation of Functions 3.3.1 Environment Monitoring This function deals with the data get from sensors, and displays the data in web pages. Implementation of the function depends on the deployment of sensors. After sensors working successfully, greenhouse environmental parameters stored in the Access database are obtained by this module. The system uses AUTO-22 greenhouse environmental data collector. According to the need of the greenhouse environmental model, twenty-one sensors are used. The attributes of the table created in the database is shown as table 1.


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Table 1. Meaning of the database table attributes

Attributes temp1 temp3 temp5 temp7 temp9 temp11 temp13 temp15 temp17 temp19

temp21

Meaning inner surface temperature of back slope 1# outdoor horizontal solar illuminance 3# Outdoor environmental temperature 5# Inner surface temperature of back wall 7# Outer surface temperature of insulation 9# Outer surface temperature of wall 11# Indoor environmental humidity 13# Indoor solar illuminance of soil surface 15# Indoor temperature of air 17# Inner surface temperature of Translucent membrane 19# Deep soil temperature 21#

Attributes

Meaning

temp2

outdoor humidity 2#

temp4

Outdoor wind speed 4#

temp6

Outdoor wind direction 6#

temp8

temp14

Inner surface temperature of second wall 8# Outer surface temperature of first wall 10# Outer surface temperature of back slope 12# Indoor soil moisture 14#

temp16

Concentration of CO2 16#

temp18

Crop canopy temperature 18# soil surface Temperature 20#

temp10 temp12

temp20

The system uses a JavaBean named Condition to set and get data from the database. DAO of the system obtains data from the table in database and stores it in a list consisted of objects of Condition. JSP pages read the list to display the data in browsers. Query statement of access: sql=select top "+pageSize+" * from temp_humi_0 where id not in (select top "+(size)+" id from temp_humi_0 order by id asc. The “pageSize” in above sentence is a variable to define the number of data item in a page. The “size” variable presents “(pageNo–1)*pageSize”. To display the data in a line chart, this system needs JFreeChart, which is an open chart drawing library on Java platform[4]. JFreeChart is programmed completely by Java Language, and designed for the use of applications, applets, servlets and JSP. The system needs the JFreeChart package to draw line chart. Add jfreechart-1.0.6.jar, gnujaxp.jar and jcommon-1.0.10.jar in lib directory. Procedure of generating chart in this system: 1). Create a dataset to include the data displayed in a line chart, which is stored in database. 2). Create an object of JFreeChart to present the chart to be shown. 3). Output the chart. 3.3.2 Environmental Prediction Calculating of each prediction module is developed by Matlab. With the component of Matlab Builder for Java, package the function of prediction module calculated in

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Matlab into a Java component. This Java component can be called in JSP web system. Taking the solar prediction module an example, package the file named shortwaveradiation.m into a file named shortwaveradiation.jar. Including this new file in the project, the system can call this prediction function. Matlab Builder for Java (known as Java Builder) is an extension of Matlab Compiler. Java Builder packages Matlab functions into one or more Java classes. Matlab functions are packaged into Java classes, and can be called by Java applications. Implementation of environmental prediction function is based on greenhouse environmental model[5]. The model integrates solar model, air temperature model, air humidity model and CO2 Concentration model to build an overall prediction model for greenhouse environment. The model needs local weather forecast information. With the forecast information, indoor solar illuminance, air temperature, air humidity, CO2 concentration, soil temperature and soil moisture can be realized. By inputting values of cloud and local time, solar illuminance prediction function

I o which has reached surface of the greenhouse, and the total flux of solar radiation I 冠层 which has reached the crop canopy. calculates the total flux of solar radiation

The function also calculates solar radiation energy absorbed and reflected by crop

Qr d- c , solar radiation energy absorbed by surface of soil Qr d- s , solar radiation energy absorbed by inner and outer surface of back slope Qr d- r i and Qr d- r o , and solar radiation energy absorbed by inner and outer surface of back wall Qr d- bi and Qr d- bo . canopy

Variables calculated in solar illuminance prediction function are needed in air temperature prediction model. The model is based on thermal balance equations such as indoor air thermal balance equation, indoor soil thermal balance equation, back slope thermal balance equation, back wall thermal balance equation and so on. Take the indoor air thermal balance equation as an example, the equation is

(1)

Greenhouse temperature prediction model can predict indoor air temperature, crop canopy temperature, surface of soil temperature and so on. Greenhouse solar illuminence prediction model can predict crop canopy flux of solar radiation. They are the known conditions for prediction of greenhouse CO2 dynamic prediction model. Mean values in hours per day of crop canopy flux of solar radiation, crop canopy temperature, surface of soil temperature, air temperature, concentration of CO2, and inner surface temperature of translucent membrane obtained by sensors, and measured values each hour per day of outdoor temperature, outdoor humidity are the known conditions for greenhouse air humidity prediction model. The function of prediction is shown as fig. 2.


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Fig. 2. Greenhouse environmental prediction model calculating flow chart

4 Conclusion The system designed by this paper can provide high-precision data of changes of greenhouse environmental parameters to greenhouse managers. The system realizes remote monitoring and prediction via Web and provides an actual method to realize precision agriculture. The system is an application system for greenhouse environmental parameters prediction. Acknowledgements. This study has been funded by Inner Mongolia Natural Science Foundation Projects (Contract Number: 20080404).

References 1. 2. 3. 4. 5.

Jin, S.: Study on remote control system for the greenhouse based on B/S model. Packaging and Food Machinery 26(3), 15–19 (2008) Zhou, N., Fang, H., Li, J.: Design and Implementation of Intelligent Business Expanding Expert System Based on JSP Technology. Guangdong Electric Power 20(3), 57–66 (2007) Sigrimis, N.: Computer integrated management and intelligent control of greenhouse. In: Fourteenth 1999, IFAC World Congress, Beijing. PRC (1999) JFreeChart API Documentation, http://www.jfree.org/jfreechart/api/javadoc/index.html Li, W., Dong, R., Tang, C., Zhang, S.: A Theoretical Model of Thermal Environment in Solar Plastic Greenhouses with One-Slope. Transactions of the CSAE (2), 160–163 (1997)

Design of Limb for Parallel Mechanism Based on Screw Theory* Zhigang Lai1, Lixin Li2, and Ping’ an Liu2 2

1 Jiangxi Technical College of Manufacturing, Nanchang, Jiangxi, 330095, China School of Mechanical and Electronic Engineering at East China Jiaotong University, Nanchang 330013, China [email protected]

Abstract. Based on the reciprocal relationship of twist and wrench in screw theory, the mathematical model for limb of parallel manipulator is established in this paper. According to the motion modes of mobile platform (translation or rotation), we concluded the geometric conditions which the prismatic joint or revolute joint must meet with by analyzing the constraint screw on the platform, which provides the background for development of parallel mechanism. Keywords: parallel mechanism; crew theory: limb; geometric condition.

1 Introduction In recent year, since parallel mechanism can offer higher stiffness and larger load capability than those of serial mechanism, it has become a hot research topic in international robotics area. However, it is very difficult to design because of the complexity of kinematics and dynamics, the diversity of limb and the coupling of architecture.[1-3] It is the most important task to meet with the DOF of the required motion for designing the parallel mechanism. In fact, DOF is the outward feature. The key is the design of constraint to implement the DOF of the required motion. The DOF of motion is objective in the limb. However, the constraint is designed in the limb by designer. And there are strict requirements to the geometric conditions which the prismatic joint or revolute joint must meet with in the limb.[4-5] Based on the reciprocal relationship, in this paper, we concluded the geometric conditions which the prismatic joint or revolute joint must meet with in the limb by analyzing the constraint screw on the platform. According to the limb, we can design the parallel mechanism which is satisfied the required movement. It is a common method to the basic design of the parallel mechanism.

2 Structural Synthesis of the Constraint Screw on the Platform Each limb should provide a constraint screw for the moving platform in the parallel mechanism. According to the constraint characteristics provided to the platform, * Supported by Natural Science Foundation of Jiangxi, China, NO. 2008GZC005. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 508–518, 2011. © IFIP International Federation for Information Processing 2011

Design of Limb for Parallel Mechanism Based on Screw Theory

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screw can be divided into twist and wrench. An arbitrary motion screw in space can include six motions at most, three translations along the X, Y, Z axes and three rotations around the X, Y, Z axes[6-8]. Twist should provide constraint for movement in space. It can be defined a r = ( sr ; rr × sr ) . In the formula, sr stands for a unit vector along axis of twist and

＄

rr for a point on the axis direction of twist. Wrench should provide constraint for rotation in space. It can be defined as r = (0 0 0; lr mr nr ) and here we have

＄

l + m + n = 1. 2 r

2 r

2 r

Twist and wrench are decided by the structural conditions of limb in parallel mechanism. So the type of constraint depends on the structural conditions of limb. According to the type of constraint, we can obtain the characteristic of limb structural. According to the difference of constraint in the limb, they can be divided into unconstrained limb, single constrained limb, double constrained limb, three-constrained limb, four-constrained limb, five-constrained limb and six-constrained limb. However, the five-constrained limb and six-constrained limb belong to planar limb which have no requirement in the space. This paper analyzes the limb types including: the limb providing only one twist, the limb with two twists, the limb with three twists; the limb with only one wrench, the limb with two wrenches, the limb with three wrenches; the limb with one twist and one wrench, the limb with two twists and one wrench, the limb with one twist and two wrenches. 2.1 The Limb with Only One Twist

＄

The basic expression of screw is

r

= (sr ; rr × sr ) . It is known from the reciprocal

of screw that the limb should consist of the five independent screws which are reciprocal with the twist . When the joint is revolute = ( s; r × s )

＄

＄＄ = s i(r × s) + si(r × s ) = s i[(r − r )× s] = 0 r

r

r

r

r

r

According to the geometric feature of vector, it should be known that there is a common perpendicular among

sr , r − rr and s , that is to say, the axis direction of the

revolute joint and the axis direction of twist must be in the same plane. When the joint is prismatic = (0; s )

＄

＄＄ = s is = 0 r

r

According to the geometric feature of vector, it should be known that

sr and s must

be perpendicular each other, that is to say, the moving direction of the prismatic joint and the axis direction of twist must be perpendicular each other. When the structural conditions of limb meet with the above requirements, the limb should provide only one twist. According to the requirements above, we should put up the limb-RRPRR shown as Fig. 1. First, we construct two parallel revolute joints,

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Z. Lai, L. Li, and P. Liu

which make sure the direction of twist. Second, we establish the moving direction of the prismatic joint and the axis direction of twist must be vertical each other. Last, the axis direction of the last two revolute joints must intersect at o point which is the point of action of sr.

Fig. 1. Structur of Limb RRPRR-1F (sr//s1//s2┴s3, s4 and s5 intersect at o, sr acting on o)

2.2 The Limb with Two Twists The basic expression of screw is

ˀ ˀ

° ® ° ¯

r1

= (sr1; rr1 × sr1 )

. It is known from the reciprocal = (sr2; rr2 × sr2 ) of screw that the limb should consist of the four independent screws which are reciprocal with the two twists. When the joint is revolute = ( s; r × s )

＄＄

⎧ ⎪ ⎨ ⎪ ⎩

r2

＄

r1 r2

＄ = s i(r×s) + si(r ×s ) = s i[(r −r )×s] = 0 ＄ = s i(r×s)+si(r ×s ) = s i[(r −r )×s] =0 r1

r1

r1

r1

r1

r2

r2

r2

r2

r2

According to the geometric feature of vector, it is known that there is a common perpendicular among

sr1 , r − rr1 and s ; a common perpendicular among sr 2 ,

r − rr 2 and s , that is to say, the axis direction of the revolute joint must pass the point which is the point of intersection with the two twists or be parallel the plane which makes sure by the two twists. When the joint is prismatic = (0; s )

＄＄＄ ⎧ ⎪ ⎨ ⎪ ⎩

r1 r2

＄= s ＄= s

is = 0 r 2 is = 0

r1


sr1 and s must

be vertical each other; sr 2 and s must be vertical each other, that is to say, the moving direction of the prismatic joint must be parallel the cross-produce of the two twists. And there is the only prismatic joint in the limb.


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When the structural conditions of limb meet with the above requirements, the limb should provide two twists. According to the requirements above, we should put up the limb-RRPR shown as Fig. 2.

Fig 2. Structur of Limb RRPR-2F (s1┴sr1×sr2s2┴sr1×sr2, o is the point of action with sr1 and sr2)

2.3 The Limb with Three Twists The basic expression of screw is ⎧ ⎪ ⎪ ⎨ ⎪ ⎪⎩

＄＄＄

r1 r2 r3

= ( sr1 ; rr1 × sr1 ) = ( sr 2 ; rr 2 × sr 2 ) = ( sr 3 ; rr 3 × sr 3 )

The limb should provide three independence twists which intersected at a point for platform. The limb constrained the three directions moving of the platform, that is to say, the platform just can be round the point to revolve. The limb should consist of three revolute joints, and that, there is only one type of the limb-RRR. According to the requirements above, we should put up the limb-RRR shown as Fig. 3.

Fig. 3. Structur of Limb RRR-3F (s1, s2, and s3 intersect at o)

2.4 The Limb with Only One Wrench The basic expression of screw is

＄

r

= (0; sr ) . It is known from the reciprocal of

screw that the limb should consist of the five independent screws which are reciprocal with the wrench.

512


When the joint is revolute

＄ = ( s; r × s ) ＄＄ = sis r

=0

r

According to the geometric feature of vector, it should be known that sr and s must be vertical each other, that is to say, the axis direction of the revolute joint and the axis direction of wrench must be vertical each other. When the joint is prismatic = (0; s )

＄

＄＄= 0 r

The equation is satisfied under any conditions, that is to say, the moving direction of the prismatic joint is independent of the axis direction of wrench. When the structural conditions of limb meet with the requirements above, the limb should provide only one wrench. According to the requirements above, we should put up the limb-RRPRP show as Fig. 4.

（s //s , s //s ×s ）

Fig. 4. Structur of Limb RRPRP-1M

1

2

r

2

4

2.5 The Limb with Two Wrenches The basic expression of screw is

ˀ ˀ

° ® ° ¯

= (0; sr1 ) . It is known from the reciprocal of = (0; sr 2 ) r2

r1

screw that the limb should consist of the four independent screws which are reciprocal with the two wrenches. When the joint is revolute = ( s; r × s)

＄

⎧ ⎪ ⎨ ⎪ ⎩

＄＄ = si s ＄＄ = si s

=0 r2 = 0

r1

r1

r2

According to the geometric feature of vector, it should be known that sr1 and

s must

be vertical each other; sr 2 and s must be vertical each other, that is to say, the axis direction of the revolute joint must be parallel the cross-produce of the two wrenches. When the joint is prismatic = (0; s )

＄

＄＄ =0 ＄＄ =0

⎧ ⎪ ⎨ ⎪ ⎩

r1

r2


513

The equation is satisfied under any conditions, that is to say, the moving direction of the prismatic joint is independent of the axis direction of the wrenches. When the structural conditions of limb meet with the requirements above, the limb should provide only one wrench. According to the requirements above, we should put up the limb-RRPR shown as Fig. 5.

Fig. 5. Structur of Limb RRPR-2M (sr1×sr2//s1//s2//s3)

2.6 The Limb with Three Wrenches The basic expression of screw is

＄＄＄

⎧ ⎪ ⎨ ⎪ ⎩

r1 r2 r3

= (0; sr1 ) = (0; sr 2 ) = (0; sr 3 )

The limb should provide three independence wrenches which are independent each other. The limb constrained the three directions rotation of the platform, that is to say, the platform just can move along the X, Y, Z axis. The limb should consist of three independent prismatic joints, and that, there is only one type of the limb-PPP shown as Fig. 6.

Fig. 6. Structur of Limb PPP-3M (s1, s2 and s3 are independent each other)

514


2.7 The Limb with One Twist and One Wrench The basic expression of screw is

ˀ = (sr1;rr1 ×sr1) ° r1 . ® °ˀ r 2 = (0; s r 2 ) ¯

It is known from the reciprocal

of screw that the limb should consist of the four independent screws which are reciprocal with the one twist and one wrench. When the joint is revolute = ( s; r × s )

＄＄＄ = s i(r×s) + si(r ×s ) = s i[(r −r )×s] = 0 ＄＄ = si s = 0

⎧ ⎪ ⎨ ⎪ ⎩

r1

r1

r2

r1

r1

r1

r1

r2

According to the geometric feature of vector, it should be known that there is a common perpendicular among

sr1 , r − rr1 and s ; sr 2 and s must be

vertical each other, that is to say, the axis direction of the revolute joint must be located in the normal plane, which contained the twist, of the wrench. When the joint is prismatic = (0; s )

＄＄＄ ⎧ ⎪ ⎨ ⎪ ⎩

r1 r2

＄ = sis ＄= 0

r1

=0


sr1 and s must

be vertical each other, that is to say, the moving direction of the prismatic joint and the axis direction of twist must be vertical each other. When the structural conditions of limb meet with the above requirements, the limb should provide one twist and one wrench. According to the requirements above, we should put up the limb-RPRR shown as Fig. 7.

Fig. 7. Structur of Limb RPRR-1F1M(s2┴sr1,s1┴sr2, s3┴sr2,s4┴sr2,s1,s3,s4 and sr2 are in the same plane

）


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2.8 The Limb with Two Twists and One Wrench The basic expression of screw is ⎧ ⎪ ⎪ ⎨ ⎪ ⎪⎩

＄＄＄

= ( sr1 ; rr1 × sr1 ) r 2 = ( sr 2 ; rr 2 × sr 2 ) r 3 = (0; sr 3 ) r1

It is known from the reciprocal of screw that the limb should consist of the three independent screws which are reciprocal with the screws. When the joint is revolute＄ = ( s; r × s )

＄＄ = s i(r×s) + si(r ×s ) = s i[(r −r )×s] = 0 ＄＄ = s i(r×s) +si(r ×s ) = s i[(r −r )×s] =0 ＄＄ = sis = 0

⎧ ⎪ ⎪ ⎨ ⎪ ⎪⎩

r1

r1

r1

r1

r1

r1

r2

r2

r2

r2

r2

r2

r3

r3

According to the geometric feature of vector, it should be known that there is a common perpendicular among sr1 , r − rr1 and s ; a common perpendicular among

sr 2 , r − rr 2 and s ; sr 3 and s must be vertical each other, that is to

say, the axis direction of revolute joint, which must be vertical the axis direction of the wrench, must pass the point which is the point of intersection with the two twists or be parallel the plane which make sure by the two twists. When the joint is prismatic = (0; s )

＄

⎧ ⎪ ⎪ ⎨ ⎪ ⎪⎩

＄＄ = si s ＄＄ = si s ＄＄= 0 r1

r2

=0 r2 = 0

r1

r3

According to the geometric feature of vector, it should be known that be vertical each other;

sr1 and s must

sr 2 and s must be vertical each other; sr 3 is independent of s ,

that is to say, the axis moving direction of the prismatic joint, which is independent of the axis direction of the wrench, must be parallel the cross-produce of the two twists. When the structural conditions of limb meet with the above requirements, this limb should provide two twists and one wrench. According to the requirements above, we should put up the limb-RRP shown as Fig. 8.

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Fig. 8. Structur of Limb RRP-2F1M

（s ┴s ,s ┴s ,s ┴s ,s ┴s , s ┴s ×s , s ┴s ×s ) 1

r3 2

r3 3

r1 3

r2

1

r1

r2

2

r1

r2

2.9 The Limb with One Twist and Two Wrenches The basic expression of screw is

＄＄＄

⎧ ⎪ ⎪ ⎨ ⎪ ⎪⎩

= ( sr 1 ; r × s r 1 ) r 2 = (0; sr 2 ) r 3 = (0; sr 3 )

r1

It is known from the reciprocal of screw that the limb should consist of the three independent screws which are reciprocal with the screws. When the joint is revolute ＄ = ( s; r × s )

＄＄ = s i(r×s) + si(r ×s ) = s i[(r −r )×s] = 0 ＄＄ = si s = 0 ＄＄ = sis = 0

⎧ ⎪ ⎪ ⎨ ⎪ ⎪⎩

r1

r1

r1

r2

r2

r3

r3

r1

r1

r1

According to the geometric feature of vector, it should be known that there is a common perpendicular among sr1 , r − rr1 and s ; sr 2 and s must be vertical each other;

sr 3 and s must be vertical each other, that is to say, the axis direction of revolute joint, which is in the same plane with the twist, must be parallel the cross-produce of the two wrenches. When the joint is prismatic ＄ = (0; s ) ⎧ ⎪ ⎪ ⎨ ⎪ ⎪⎩

＄＄＄

r1 r2 r3

＄ = si s ＄= 0 ＄= 0

r1

=0


517


sr1 and

s must be vertical each other, that is to say, the axis moving direction of the prismatic joint must be vertical the axis direction of the twist. When the structural conditions of limb meet with the above requirements, the limb should provide one twist and two wrenches. According to the requirements above, we should put up the limb-RRP shown as Fig. 9.

Fig. 9. Structur of Limb RRP-1F2M (s1// s2//sr2×sr3, s3┴sr1 plane )

，s , s 1

2

and sr1 are in the same

3 Example of the Application Since it is regular for the geometric conditions of the joint in the limb, which connects fixed platform with mobile platform, the limb, which meets with the geometric conditions, can be used to establish the parallel mechanism which be satisfied with the mode of motion. Now take the limb Fig. 7 as an example to establish the 3-2T1R parallel mechanism as shown in Fig. 10. The limb in Fig. 7 provides one twist along the Z-axis, which constrain the moving along the Z-axis, and one wrench along the Xaxis, which constrain the rotation around the X-axis. Thanks to the wrenches which provided by the three limbs are dependent in the XY plane, it should constrain the

Fig. 10. 3-2T1R parallel mechanism

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rotation around the X-axis and Y-axis, that is to say, the platform just can revolve around Z-axis and move in the XY plane.

4 Conclusions According to analyze the type of limb and the type of restraint in screw theory, it is given a general design method of limb in the parallel mechanism. And that, it obtained the geometric conditions which the prismatic joint or revolute joint of over-constrained parallel mechanism must be meet with. The method analyzed from basic concept of the reciprocal produce in screw theory. It should make sure be general and pragmatic. It is a common reference value to the basic design of the parallel mechanism.

References 1. 2.

3. 4.

5. 6.

7. 8.

Ball, R.S.: A Treatise on the Theory of Screws. Cambridge University Press, Cambridge (1900) Fang, Y., Tsai, L.-W.: Structure Synthesis of a Class of 4-DoF and 5-DoF Parallel Manipulators with Identical Limb Structures. The International Journal of Robotics Research 21(9), 799–810 (2002) Herve, J.M., Sparacino, F.: Structural synthesis of “Parallel” Robots Generating Spatial Translation. IEEE, Los Alamitos (1991) 7803-0078/91/0600-0808$01.00 Wang, J., Gosselin, C.M.: Kinematic Analysis and Singularity Loci of Spatial FourDegree-of-Freedom Parallel Manipulators Using a Vector Formulation. ASME Transactions, Journal of Mechanical Design 120(4), 555–558 (1988) Tsai, L.-W.: Systematic Enumeration of Parallel Manipulators. Technical Research Report, T.R. 98-33 Herve, J.M., Karoutia, M.: The Novel 3 - RUU Wrist with No Idle Pair. In: Proceedings of the Work-shop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators, Quebec, Canada, pp. 284–286 (2002) Kong, X., Gosselin, C.M.: Type Synthesis of Three - Degree - of - Freedom Spherical Parallel Manipulators. The International Journal of Robotics Research 23(3), 237–245 (2004) Karouia, M., Herve, J.M.: Non – over-constrained 3 DOF Spherical Parallel Manipulators of Type; 3 –RCC, 3 – CCR, 3 – CRC. Robotica 24, 85–94 (2006)

Design of Non-Full Irrigation Management Information System of Hebei Province Based on GIS Junliang He, Yanxia Zheng, and Shuyuan Zhang Department of Resource & Environment, Shijiazhuang University, Shijiazhuang 050035, China [email protected]

Abstract. Combined the data of irrigation experiments in Hebei Province, The paper built an integrated system which integrates data input and output, management, analysis and decision. The system is based on GIS (Geographic Information System), DSS (Decision Support System) and crop water production formula. With the establishment of the system, the modern management level of non-full irrigation will be advanced, and the allocation of soil and water resources will be optimized. By analyzing the system demand, the framework of non-full irrigation management information system was proposed, and the main function of the system was designed in the article. Keywords: Geographic Information System, Non-full Irrigation, Hebei.

1 Introduction Water is the basic natural resources for social and economic development, and also the important strategic resources. With the low per capita water resources and uneven space-time distribution, the contradiction between supply and demand of water is still very prominent in our country. The annual amount of water shortage reaches 300-400 billion cubic meters in China which is one of the water shortage countries. Hebei is one of the serious water shortage provinces in China, the per capita water resources is oneeighth of the national average level. In this area, 90 percent of agricultural water is used for crop irrigation, but the actual utilization rate of water resources is only about 45 percent, lower than 50 percent which is the utilization rate of the most water shortage country. There exists a large water saving potential in Hebei Province behind the phenomenon of water shortage [1]. Therefore, the development of high efficiency and water saving agriculture is one of the main measures to alleviate water shortage, and to promote sustainable development of agriculture in this area. As an advanced mode of water saving irrigation, the non-full irrigation is studied from various aspects in recent years. According to the analysis of water consumption of wheat in North China Plain, Changming Liu etc reveal wheat water effect and water requirement [2]. Based on the irrigation experimental data in Linxi and Wangdu, Shaoyuan Feng etc use multiple regression analysis method to determine the sensitive index of the model [3]. Combined irrigation experimental data in Wangdu, Luhua Yang etc discuss the solution method for two dimensional dynamic programming D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 519–525, 2011. © IFIP International Federation for Information Processing 2011

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of the Jensen model [4]. According to the irrigation experimental data in Gaocheng, Wangdu and the central experiment stations, Zunlan Luo etc select Jensen, Minhas, Blank, Stewart and Singh models to analyze water production function of maize in Hebei Province [5]. At present, the information of non-full irrigation in this region is acquired and managed mainly by traditional manual work, the effective information management, maintenance, and sharing will be impossible to realize. To develop water saving agriculture, the key is to integrate irrigation experimental results and information technology, establish the decision support system of water saving irrigation, promote the modern management level of non-full Irrigation, and optimize the allocation of soil and water resources.

2 General Structure Design Facing the decision making managers and professionals, the non-full irrigation information has spatial and temporal distribution properties. The information involves topography, hydrogeology, river system, weather, vegetation, soil and other factors in the agricultural area. The GIS has efficient ability of spatial data management, and flexible ability of comprehensive analysis. DSS can provide the decision support environment of model construction, process simulation, and effectiveness evaluation for the management [6]. Therefore, In view of the professional model, GIS and DSS technology, considering practicality and scalability, the system architecture based on C/S mode is proposed. The system uses SQL Server which is a large relational database to manage the spatial data and non-spatial data, and adopts GIS software

Fig. 1. The basic structure of non-full irrigation management information system

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development platform MAPGIS 7. 0and Microsoft Visual Basic 6.0 to develop. The basic framework of the system is shown in Figure 1.

3 Database Design The main function of database system is to manage, store related information, and provide the support for the establishment and auxiliary management of the non-full irrigation schedule design. The database design is the basis for development application system, including irrigation information database and geographic information database two categories. 3.1 Irrigation Comprehensive Information Database In this paper, according to the need of the non-full irrigation management, and combined the irrigation experimental data of agricultural experiment stations, the Table 1. The content of agricultural irrigation comprehensive information database

Data type Basic information of irrigation station

Irrigation experimental data

Basic information of meteorological station Meteorological monitoring data

Water bulletin

Data content site code, site name, latitude, longitude, annual average temperature, annual average evaporation, soil, field moisture capacity, soil capacity, organic matter content, total nitrogen, total potassium, total phosphorus, salt content, groundwater depth, groundwater salinity etc hydrometric station code, hydrometric station name, crops name, growth stage name, beginning year, beginning month, beginning date, ending year, ending month, ending date, growth period day, precipitation, survey month, survey date, soil humidity, irrigation month, irrigation date, irrigation quota, water consumption, yield, method etc site code, site name, altitude, longitude, latitude etc site code, site name, latitude, longitude, altitude, year, month, day, time pressure, time temperature, time relative humidity, time precipitation, time wind speed, daily average sunshine time, daily extreme temperature, minimum relative humidity etc surface water resources, groundwater resources, water supply, water consumption, effective irrigated areas on farmland etc

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comprehensive information database is established, which includes irrigation data, meteorological data and water data etc (Table 1). At the same time, in order to realize the connection of the comprehensive information database and irrigation geographic information database, the shared field parameters are also taken into account in the database design phase. 3.2 Geographic Information Database Geographical information database is the basis for the function realization of GIS. It mainly accomplishes graphic data management, retrieval and query, as well as the spatial analysis and evaluation of thematic data. Taking advantage of the vectorization function of Mapgis, the author obtained the administrative map of Hebei province (surface), the main water distribution (line), irrigation experiment stations distribution (points), weather station distribution (points) and so on. In order to connect with the database of irrigation comprehensive information, using the property management function of Mapgis, the author modified the geography information table on the digital map, and added the corresponding shared field parameters. In addition, the modify functions of geographical information database are not provided for ordinary users in order to prevent mistakes causing by modification in the geographical information database.

4 Model Base Design According to the model of application analysis which is called during decision analysis, model base system presents data demand and storage format requirement to the database system. The optimization of the irrigation schedule model is the core of decision support system. In order to achieve high yield targets, under the limited irrigation quota conditions, the optimal allocation of the irrigation quota in timing and quantity is realized. Using the crop-water model (MCRW) as constraints or objective function, and the crop water consumption as the variable, the relation between the yield and the water deficit in different growth stage and different degree is revealed in this system. With the linear programming method, the optimal solution is obtained through the linear processing of the objective function, the calculating processing is simplified, and the optimization of the non-full irrigation schedule design is realized. A series of professional models is established in this system, involving reference crop water requirements, crop water requirements, irrigation quota calculation and assessment of crop water deficit. Base on the analysis of the spatial and non spatial information, enlightened by the professional knowledge and experience, decisionmakers can evaluate and predict water supply and demand, and establish water saving irrigation strategies.

5 Function Module Design 5.1 GIS General Functions Data management: With the data management platform of entire system, a series of operations are accomplished, such as daily management and maintenance of database,

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data edit, data import and data export. The function of the system includes database management, documentation management, data manipulation and metadata maintenance and so on. Query and retrieval: Based on the function of the visual graphic display, with the aid of functions such as roaming, zooming and eagle-eye, the location, query and browsing for the spot, line, plane and other geographic features are realized. According to the combination of data item and any logical expression, users can query and retrieve the attribute data. Statistical analysis: The system may carry on the statistical calculation to the related data, including maximum value, minimum value, average value, histogram computation and so on. Special output: According to the need, users can choose the layer to output water supply and demand maps, agricultural weather information maps and other thematic maps, can also output all kinds of the irrigation resources parameters in the form of statistical charts and text statements, including known data and the result data. 5.2 Professional Analysis Functions Analysis of crop water requirement: Based on the Penman formula, the crop water requirements at any day can be calculated, and the daily crop water requirements and the monthly crop water requirements from 1991 to 2000 in the experimental Station of Hebei Province can be queried. Using the reference crop evaporation quantity to calculate the crop evaporation quantity, the crop water requirements can be calculated. Based on the experimental data of crop water requirements, the query of the different crop coefficient in different region or during whole growth period can be realized. According to the soil condition, the irrigation quota before planting and the irrigation quota in each growth phase can be calculated. Optimization of the irrigation schedule design: Using the existing analysis results of irrigation data, the crop - water model which suit for the different corps in different region of Hebei province, and the sensitive index in each growth phase can be queried. The irrigation quota, the crop water consumption corresponding the maximum yield treatment, the relative yield and the relative evaporation in each growth phase, the effective rainfall and other data can be set by users, also can be queried from the functional modules such as the basic information and analysis of crop water requirements. Through inputting the limited water supply (irrigation quota), the water allocation optimized strategy with different irrigation quota can be calculated, and the comparative analysis of the economic scale irrigation quota can be carried on, according to the relations between each kind of irrigation quota and optimal relative analog output. 5.3 System Maintenance and Help Functions System login: In order to ensure the security of the system, users must input the user’s name and the password to entry the system. System help: The functions of system help include system description and user guide and so on.

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6 Conclusion Combined GIS and professional models, with the development of the non-full irrigation management information system, the graph and the attribute data are queried reciprocally; the irrigation data and agricultural resource are managed scientifically. The partial interface of the system is shown in Figure 2. Based on this, according to the water resources condition in different region and the different crop water deficit condition, the optimal allocation strategy of the limited irrigation quota in the timing and quantity is proposed. Taking advantage of this system, decisionmakers can grasp all kinds of information in crop area, realize the digital, systematic and scientific of the agricultural irrigation resource management, and advance the effective development and sustainable utilization of the agricultural water.

Fig. 2. The partial interface of the system

References 1. 2. 3.

Wang, J.: Utilization and Countermeasures of Water Resources in Hebei Province. Industrial & Science Tribune 7(2), 85–86 (2008) Liu, C., Zhou, C., Zhang, S.: Study on Water Production Function and Efficiency of Wheat. Geographical Research 24(1), 1–10 (2005) Feng, S., Luo, Z., Zuo, H.: The Study of Water Product Function of Winter Wheat in Hebei Province. Journal of Irrigation and Drainage 24(4), 58–61 (2005)

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Yang, L., Xia, H., Wang, F.: Application & Solution of Jensen Model In Unsufficient Irrigation Schedule. Irrigation and Drainage 21(4), 13–15 (2002) Luo, Z., Feng, S., Zuo, H.: Preliminary Study on Water Production Function for Summer Corn in Hebei Province. Water Saving Irrigation 1, 17–19 (2006) Ge, A., Li, C., Yang, C.: Primary Study on Building Water-saving Agricultural Decision Support System Based on GIS. Remote Sensing Technology and Application 19(5), 392– 395 (2004)

The Monitoring System of Water Environment Based on Overlay Network Technology∗ Xueliang Fu, Changyou Li, Buyu Wang, Honghui Li, Hailei Ma, and Dongnan Zhu Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China [email protected]

Abstract. At present, although methods of automatic, periodical manual data collection have been adopted in some areas, there are many problems existing in these methods. This paper proposes a new framework for online monitoring of water environment based on overlay network technology. Especially, we build the multi-level overlay network, which consists of the GPRS networks, mobile networks and internet networks. Following this, a multi-dimensional data cube for water environment is established using the ETL process with the input of complex heterogeneous data collected. Thereby, the framework of data center is established for on-line early warning of water environment and data analysis and processing. This framework has been put into use in Wuliangsuhai for the on-line real-time water monitoring tests. The results show that the overlapping network architecture is effective and on-line analysis and early warning of heterogeneous data is efficient. Keywords: WSN, Overlay network, ETL, Multidimensional data cube.

1 Introduction Currently, water-quality monitoring in Wuliangsuhai is mainly performed manually [1]. Researchers in related institutes reside in Wuliangsuhai, and gather water-quality data manually during the ice-absent period. The main method to gather data relies on portable instruments. It is difficult to meet the needs of comprehensive monitoring, analysis and early warning in Wuliangsuhai for water-quality. Therefore, the comprehensive development of research and eco-social benefit is seriously restricted [2]. There are mainly four issues in the above traditional method. Firstly, researchers had to be outside for long time, thus, they cannot conduct large-scale experimental analysis using tools and computing environment. Secondly, the continuity of data collection is poor, and a sample space is very limited. In the ice-absent period, one can only collect data once or twice per day (once at noon, or twice in the morning and evening respectively). One cannot collect data at night as well as in freeze-up period. ∗

The research is supported by Chinese Natural Science Foundations (50969005,40901262) and by Specialized Research fund of High Education for Inner Mongolia (Njzy08046).


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So, the number of the achieved water-quality data is no more than 200 records. Therefore, it is impossible of widely data mining and analysis owing to the significant shortage of data. Moreover, the quality of the collected data cannot be guaranteed. Thirdly, using the manual-collecting method, the quality of data is effected strongly by the professional proficiency of the researchers and the usage of the equipment. As a result, dirty data may appear and cannot be analyzed and rectified using other timesequential data. Finally, the collection area is very limited. Due to the broad water area of the Wuliangsuhai Lake, it is impossible for the residing researchers to collect data at the same time from multiple locations far away from each other. As a result, there is no way to analyze and compare data in terms of time scale and location. The current trends to resolve these issues is to use the framework of cloud services, with which data is online automatically assembled, real-time transmitted using overlay networks. Decision of knowledge is made in a data center. Wireless sensor networks (WSN) are the task-oriented wireless networks which consist of a number of wireless sensor nodes [3]. WSN integrates multiple area technology, including sensor technology, embedded computing technology, modern networking, wireless communication technology, distributed information processing technology and others. In WSN, various micro sensors take responsibility of on-line monitoring target; the embedded computing resources take care of processing data obtained by sensors; the related information is send to the remote user data center using wireless communication networks. This technology can be broadly applied in the military defense, industrial and agricultural controls, urban management, biological medicine, environmental monitoring, disaster relief, antiterrorism and remote control in dangerous areas. It is attractive in both academy and industry [4]. This paper studied the design of monitoring system for environment of water based on the overlapping networks in order to deal with the issues about Wuliangsuhai water-quality monitoring.

2 Main Contributions 2.1 Architecture Our proposed system is mainly composed of overlapping network communication subsystem, the water-quality sensor acquisition subsystem, multi-dimension data analysis subsystem [5] and solar power subsystem. The issues are addressed using multi-layer overlay network technologies on inter communication among networks, such as 485 network, GPRS network, PSTN network and computer networks. In this system, the task-oriented data-collecting network is constructed using water quality sensors and data acquisition equipment based on wireless sensor network technology; the multi-dimensional data analysis center are established based on OWB and ETL technologies for early warning and comprehensive analysis of instantaneous data and historical data; solar power system is employed for 24-hour uninterrupted power supply all day long and thus provide guaranteed environments for the good performance of the entire system. System architecture is shown in Figure 1.

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Fig. 1. The system architecture

2.2 Overlay Network Design A networking framework which enables multi-network integration must be explored in order to realize a distributed resource management platform. This platform can provide for the application layer reliable and efficient resource searching and localization service through monitoring link status, shielding network failures and changes. Also, this platform can provide for application service such as network routing optimization through node detection of network paths and performance. The real-time signal data on water quality parameters is achieved through the data acquisition subsystem. The collected signal data is first send to a GPRS-wireless hybrid communication system via a RS485 network, and then transfer to the data center with fixed IP address by the GPRS wireless network. In the data center, the signal data is translated, analyzed, processed and stored. The TCP multicast protocol, i.e., “center to multi-point”, is adopted for reliable and transparent data transmission. Multiple-layer hybrid overlay networks are constructed between each sensor node, which registers as an online communication node and the super node in the data center with a fixed IP. The super node manages the whole WSN. Each facility, which is eligible for the WSN, can log in, exit and abnormal exit through this subsystem. This subsystem is important for the establishment of a reliable network, which provides bidirectional data transmission channels and communication links for the transmission of signal data and control signaling. The layered hybrid network architecture is exploited in the whole network framework. The whole network is divided into four layers: the service layer, the core layer, the access layer and the link layer.


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2.3 Data Stream Processing The Data center architecture is shown in Figure 2. The data stream processing includes two steps: receiving data stream and sending data stream, which are described as follows: 1) Receiving data stream i. The on-line real-time data on water quality parameters is send to the data center in the sampling frequency using the data acquisition subsystem. The acquisition subsystem consists of water-quality-parameter sensors, filters and A / D converters. ii. After receiving the real-time data, the data center analyzes the data logically, eliminate the dirty data, and store the qualified data in the transient database. iii. Water-quality analysis-type database or data warehouse, which is suitable for analysis and statistics, is built from the data in transient database using the ETL tools combined with the functionalities of operations / scheduling of enterprise database. iv. Various analysis products needed by users can be generated using the database products for analysis (BI data warehouse reporting engine) together with the waterquality analysis-type database or data warehouse.

Fig. 2. Data center architecture

2) Sending data stream i. A user can log in the data center using the mobile terminal equipment (e.g., mobile phones, PDA, etc.) or the water quality data viewer on a fixed terminal. Through

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the data center, the user can transmit the acquisition subsystem in order to set the sampling period and other parameters of the acquisition system. ii. With the help of the data center for sending the control information, an eligible user can turn on or off the power supply system, or remote manage the power supply system.

3 Experiment Setup and Discussion Firstly this paper deployed the PH-value and ORP and oxygen-content sensors in the Wuliangsuhai Lake. Then, the real-time, automatic data acquired with adjustable sampling frequency has been achieved using the proposed water-quality sensor acquisition subsystem, the overlay network transmission subsystem, and the data storage and processing subsystem. Thereby, the water-quality parameter data can be processed and analyzed in a very short time. The software in PLC (Programmable Logic Controller) exploits the means of center-to-multi-point communication in the water-quality-parameter signal acquisition subsystem in Wuliangsuhai; the Baud Rate is 9600; the survivable mechanism is achieved through the heart-rate packet; the communications signaling employs ASCII-code signaling. The connections between the PC software and the data center are established using TCP/IP protocol. The unity of the user programming interface is approached through encapsulating the Socket package in the system level and the integrated converged communications from the data link layer, the network layer and the application layer. The data center is built based on Oracle database, where the transaction concurrency mechanism and trigger mechanism are used, and the data OLTP is achieved through the job scheduling. Oracle DWB is used to build the data warehouse which provides analysis environments. All analysis products and user UI are implemented using the B/S framework; the system is built using the SSH enterprise multi-layer computing framework. After the deployment in practice and the simulations, it is found that the proposed overlay network framework is sensitive very much to the strength of the data signals. The instantaneous packet loss may suffer when the signals become weak, which results in dirty data. However, due to the relatively high sampling frequency, this can be ignored in the context of the research and the application on water environment monitoring only if the data is accurate in the time granularity of an hour.

4 Conclusion This paper proposed and implemented the Monitoring system of water environment based on overlay network technology in order to deal with the issues on the current water environment monitoring in the Wuliangsuhai Lake. Through the field deployment of our proposed system, simulation results verify that the effectiveness of our design. It solves a series of issues existing in the current monitoring method mentioned before. The future work is to investigate the issues related to data processing and data reliable transmission both in academy and in practice.


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References [1]

[2]

[3] [4] [5]

[6]

Seelig, H.D., Hoehn, A., Stodieck, L.S., Klaus, D.M., Adams III, W.W., Emery, W.J.: Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants. Remote Sensing of Environment 11(2), 445–455 (2008) Ross, B., Steiner, G., Kiesshauer, Bradter, M., Cammann, K.: Instrument with integrated sensors for a rapid determination of inorganicions. Sensors and Actuators 27, 380–383 (2009) Tatyana, B., Thomas, A.C., Thomas, H.C.: A sensitive nitrate ion-selective electrode from a pencil lead. Journal of Chemical Education 82(3), 439–441 (2009) Wang, B., Li, M.: A clustering Algorihm Based on Latent Semantic Model. In: IEEE ICACIAP 2009, October 2009, pp. 44–48 (2009) Cooley, P.M., Barber, D.G.: Remote Sensing of the Coastal Zone of Tropical Lakes Using Synthetic Aperture Radar and Optical Data. Journal of Great Lakes Research 29(2), 62–75 (2003) Wang, Y., Dong, W., Zhang, P., Yan, F.: Progress in Water Depth Mapping from Visible Remote sensing Data. Marine Science Bulletin 26(5), 92–101 (2007)

Design of Rotary Root Stubble Digging Machine Based on Solidworks∗ Xinglong Liao1, Xu Ma1,2, and Yanjun Zuo1 1

College of Engineering, South China Agricultural University, Guangzhou, P.R. China 2 Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, P.R. China [email protected], [email protected], [email protected]

Abstract. In the paper, the necessity of root stubble harvesting and recycling was put forward from the perspective of biomass energy utilization. To accomplish mechanized harvesting on root stubble, a rotary digging machine was designed based on parametric modeling software Solidworks. Firstly, parts were built under entity modeling module, and then assembled to 4 main mechanisms in assembling environment. Secondly, mechanisms including frame, transmission mechanism, suspension mechanism and digging mechanism were assembled together to establish the whole prototype on which interference checking was done. Through manual change of the transmission chain’s installation position, the digging mechanism was able to shift between reverse and forward rotation according to different soil conditions. Finally, relevant 2-D engineering drawings were generated for manufacture. The paper provides methodological reference for the design of similar machines and preparation for further simulation and analysis of the designed models. Keywords: Root stubble, Rotary digging machine, Solidworks, Virtual design.

1 Introduction As one of the main food crops in China, corn’s significance is only next to rice and wheat. The perennial planting area is about 25 million hm2 and annual yield is up to 120 million tons[1]. As by-product of corn planting, the treatment of these root stubble is a tough task to peasants, especially in busy farming seasons. Moreover, there exist many deficiencies in traditional treating ways of root stubble. Leaving them alone will hinder subsequent seeding operation; burning them up will generate a lot of smoke harmful to the environment; burying through plowing will be inefficient; and mechanized shattering will consume large amounts of energy. Stalk and root stubble of corn is a kind of clean fuel with high heating value and low sulfur and is one of the most potential green renewable energies. As energy crisis and environmental pollution are more and more concerned in the world, the task to explore new energy and material as a replacement of petroleum is urgent. So it is necessary to harvest and recycle ∗

The research is supported by National High-tech R&D Program (863 Program) (project number: 2009AA043604).


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root stubble for the sake of providing raw material for biomass transformation and utilization. In the study, Solidworks2009 was applied for the design of rotary root stubble digging machine. With this software, 3 dimensional entity models of each part can be designed and assembled together easily and interference between components can be checked conveniently. Therefore, before manufacture of physical prototype, sufficient assembly and test can be done on simulated models, promoting the standardization, normalization and serialization of the design work.

2 Characteristics of Solidworks Solidworks is the first 3 dimensional CAD software developed on windows operating system, and due to its powerful functions, characteristics of easy to learn and easy to use, it is widely applied in mechanical design. With parametric feature modeling technology, different entities can be created, meeting most requirements of engineering design; with single internal database, all data is related with each other, modifications on dimension in any module will automatically reflect in other modules; in 3 dimensional assembly module, transmission relationship between components can be dynamically simulated. Solidworks is especially suitable for product development, as it is able to shorten product design cycle, improve design quality and reduce cost. Soliworks has become one of the mainstream software in mechanical design and modeling[2].

3 Design Process of Rotary Root Stubble Digging Machine Using Solidworks 3.1 Scheme Design The machine consisted of 4 main parts including frame, transmission mechanism, suspension mechanism and digging mechanism. The machine was hitched to tractor with suspension mechanism. During operation, power of tractor was transmitted to input shaft of the machine through universal joint from the tractor’s PTO (power take off) shaft. Then via bevel gear pair and bevel-shaft, the power was transmitted to flank chain sprockets. Finally, flank chain sprocket drove the driven sprocket which was fixed on the digging mechanism, so the digging mechanism rotated with the driven sprocket synchronously. Motion of the digging mechanism was structured by two components. While advancing with tractor (pulled by tractor), it also rotated around the self blade shaft. During working process, root stubble was dug out of soil and tossed upward by the rotating blades, and then the root stubble hit the baffle board and fell behind the machine. After a few days of exposure under the sun, these root stubble were picked up by the matched picking machine which could finally get clean root stubble as there was soil removing mechanism on the machine. In this paper, the task was to dig the root stubble out of soil and the subsequent picking procedure was not discussed here. The gear box had 1 input shaft and 2 output shafts rotate at opposite directions. To meet different tillage requirements, the digging mechanism can be switched between reverse rotation and forward rotation through manual changes of the transmission

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chain’s installation position from left to right. Noted that though there were two pairs of flank sprockets, there was only one strip of chain, so only a pair of flank sprockets (either the right pair or the left pair) was at work at a time. Transmission sketches of reverse rotation and forward rotation were shown in (a) and (b) of fig.1 respectively.

(a)

(b) Fig. 1. Transmission sketches 1. Input shaft; 2. Gear box; 3. Flank sprocket (right); 4. Transmission chain; 5. Digging mechanism; 6. Flank sprocket (left)

3.2 Part Design[3-6] Part design is the basis of 3 dimensional virtual design. In Solidworks, features are created from ways such as extrude, revolve, sweep, etc., and then combined together according to constraint relations to form parts. For example, the creating process of the bevel gear used in the machine was: create the basic feature by revolving the 2-D sketch around an axis(revolve) → create gear groove by removing material between the two profiles(loft-cut) → array gear groove around the axis(circular pattern) → create hole and keyway by cutting material(extrude-cut) → complete. The process is shown in fig.2.


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Fig. 2. Creating process of the bevel gear

Parts of the digging machine were created one by one and then saved in the same file folder, as this would make the file management more convenient especially in the subsequent assembly manipulation. During the design process, relationships among features must be taken into account. Generally, according to the order in which features are created, features and their relationships are listed in FeatureManager design tree on the left side of the interface. And for the convenience of feature modification, models can be zoomed in and out, freely rotated, hided and suppressed. 3.3 Assembly Design After parts design was completed, parts (or components) and necessary mates were inserted into assembly environment to form assembly models. Mates create geometric relationships between assembly components and define spatial position of one component relating to another. There are many mate types available in Solidworks such as coincident, parallel, perpendicular, tangent, concentric, and so on. For the machine, according to the function of each mechanism, parts were assembled to 4 subassemblies including frame, transmission mechanism, suspension mechanism and

Fig. 3. Assembly of the gear box

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digging mechanism. Then, these 4 sub-assemblies were combined together to form the complete assembly of root stubble digging machine. The gear box and the whole assembly were shown in fig.3 and fig.4 respectively.

Fig. 4. Whole assembly of the machine

3.4 Interference Detection Interference detection is one of the most important functions of Solidworks which can rapidly determine whether there is any interference between components and between sub-assemblies (a sub-assembly is treated as a single component). Here, the whole assembly was checked for interference, and according to analysis results, relevant details of parts and constraint settings between components were modified. The procedure was repeated until there wasn’t any interference, as shown in fig.5.

Fig. 5. Interference detection on the machine

3.5 Generation of 2-D Engineering Drawings After above steps, 2-D engineering drawings were generated from corresponding parts and assemblies in the drawing module, and automatic dimensioning was done in


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each drawing. Noted that 3-D models and 2-D engineering drawings were related with each other, namely any modification of dimensions made in 3-D part and assembly module would be reflected in drawing module and vice versa. Some necessary annotations such as weld symbol, geometric tolerance, surface finish symbol and BOM (Bill of Material), etc. were inserted into drawings as these were required for manufacture. Completed engineering drawings were saved in default file format of Solidworks and DWG format which was recognizable by AutoCAD. 2-D projection drawing of the whole machine was shown in fig.6.

Fig. 6. 2-D projection drawing

4 Conclusions (1) The necessity of mechanized root stubble harvesting and recycling was put forward from the perspective of biomass energy utilization, considering the traditional treating ways and its ingredient of high heating value and low sulfur. (2) Soliworks was applied to accomplish parts design, assembly design, interference detection and generation of 2-D engineering drawings. Results showed that the design was reasonable and feasible. (3) The created parts and assembly will be models for subsequent simulation and analysis if necessary. The study provides theoretical foundations and methodological references for the application of virtual prototype technology on the development of new agricultural machinery.

Acknowledgements The research is supported by National High-tech R&D Program (863 Program) (project number: 2009AA043604).

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References 1. 2. 3.

4.

5. 6.

Han, Z.: The Study of Realization Way on Corn Mechanization. Journal, Farm Machinery (05), 45–46 (2010) (in Chinese) Zhou, D., Liu, X., Lu, W.: Application of Solidworks Software on the Design of Agricultural Machinery. Journal, Modernizing Agriculture (10), 42–43 (2006) (in Chinese) Zhu, K., Ning, E., Zhao, M., et al.: Virtual Design and Experiments of 9QS8 Forage Harvester Based on Solidworks. Journal of Agricultural Mechanization Research (11), 137– 139 (2009) Yu, J., Kong, X., Huang, S., et al.: Virtual Design of Soil-processor in Rice-seedling Raising-by-plates by SolidWorks. Journal, Packaging and Food Machinery 24(2), 31–34 (2006) (in Chinese) Yang, W., Guan, C., Wu, M., et al.: Feature Modeling and Assembling. Conjunction Design on Precision Seeder by Software Solidworks (3), 110–113 (2006) (in Chinese) Zhan, D.: Solidworks Baodian. Publishing House Of Electronics Industry, Beijing (2008) (in Chinese)

Design of the Network Platform Scheme Based on Comprehensive Information Sharing of Zigong City’s Characteristic Agriculture Wen Lei, Hong Zhang, and Lecai Cai School of Computer Science, Sichuan University of Science & Engineering, Zigong, P.R. China [email protected]

Abstract. A network platform scheme targets the Zigong City’s characteristic agriculture is designed, which is according to the actuality of characteristic agriculture, the requirements of Comprehensive Information sharing, and the status of city’s network topology. In the scheme, many solutions are given out, such as the network architecture, distributed data storage, remote diagnosing, expert decision-making, comprehensive information sharing, distance learning & training, information managing, and the single sign-on logging, etc. Finally, the capability and security of the network scheme is analysed and summarized. Keywords: Agriculture, Comprehensive information sharing, Network platform, distance learning & training, Network architecture.

1 Introduction Agricultural informatization is the foundation and important way for modern agricultural development, it can reduce the investment, improve the quality and quantity of the agricultural production, reduce the influence of natural disasters, accelerate agricultural circulation, guide agricultural production and consumption. In market environment, it can help to optimize agricultural resources allocation, reduce market risk, accelerate the spread and popularization of agricultural technology, promote agricultural sci-tech personnel training, and improve the international competitiveness of agricultural products. The network is an important way to realize the agricultural informatization. This paper will aim at Zigong’s characteristic agriculture needs to build a comprehensive information sharing network platform. The platform is an agricultural comprehensive information sharing platform, which includes many functions, such as crop condition monitoring, remote diagnosing, expert decision, comprehensive information sharing, information release and management, products display, market information and instant communication function, etc. The platform is an important tool for users to store, look up and share information, which can collect the internal and external information, then classify and store them in database server. Through the platform, the merchants and agricultural leading department and farmers can share the information. The platform can help merchants and households easily to get market and product information, assist agricultural experts and agricultural leading department to do real-time guides for agricultural production. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 539–546, 2011. © IFIP International Federation for Information Processing 2011

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2 Zigong Characteristic Agriculture Status Zigong city locates in the South of Sichuan Basin, is a moist monsoon climate featuring subtropical zone. Its climate is beneficial to develop agriculture. According to the characteristics of Zigong, the city government has mapped out a regional planning of Zigong characteristic agriculture. The characteristic agricultural production is relatively concentrated, and gradually formed a certain scale of industrialized production pattern. At present, the main characteristic agriculture has more than 10 kinds, with the regional distribution in the area two district and four counties. But, in the characteristic agricultural production, there are still some problems, such as follows: (1)The quantity of agricultural sci-tech personnel is less, and most of them live in city, once there are some technological difficulties in production, farmers could hardly obtain the solution timely; (2)In a broad area, farmers are scattered here and there, it is difficult to concentrate to popularize agricultural scientific and technological knowledge, in addition, the agricultural leading department is also hard to grasp agricultural production status, and provide technological guidance;(3)It is not sufficient and timely to take the products market information, farmers are hardly to according the market demand to obtain the maximum economical efficiency;(4)The sales channel is single. Currently, the main sales mechanisms is merchants according to their own demands to buy produce from farmer, on the contrary, due to the lack of market information, farmers can hardly sale their produce to merchants actively, which is easily to cause the product backlog. To solve the above problems, it is the effective means to build a comprehensive information sharing platform. Through the platform, the farmers can easily gain agricultural science and technological information and market information, the agricultural leading department can also easily grasp the agricultural production status and guide agricultural production.

3 The Overall Design of Sharing Platform Scheme The information sharing platform is a distributed system structure. The planting and breeding belt establish their own regional information center, with Web integration technology, regional information is integrated to the sharing platform of city agricultural information center. The sharing platform through the city telecom network communicates with the regional information center. 3.1 The Design of Network Topology The platform network is constructed to a distributed network structure, the city agricultural information center LAN links to the regional information center network by city telecom network. Remote users can access the sharing platform through Internet, and local users through the city telecom network access the sharing platform or corresponding regional information center. The network topology is shown in figure 1.

Design of the Network Platform Scheme Based on Comprehensive Information

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Fig. 1. The Network Structure

3.2 The Design of Sharing Platform Scheme According to the city characteristics agriculture information sharing demand, the platform construction tasks can be divided into 5 subsystems, includes expert system, the remote diagnosis system, information collection and release system, remote learning and training system, and single sign-on(SSO) system. The sharing platform structure is shown in figure 2. Among them, the front 4 systems will be independently developed, this scheme focuses on the single sign-on system.

4 The Detailed Design Scheme In practical applications, most farmers have low level of computer skill, they can only do simple network operation, and most time, the characteristic agricultural planting and breeding farmers are concerned with the agricultural science and technology and the basic pest control information, therefore, the regional information center is the main visit position. According to the practical needs, in sharing platform design, the characteristic agriculture basic information platform and comprehensive information sharing platform are combined the scheme. Each regional information center constructs its basic information platform, and in agricultural science and technology center set up a comprehensive information sharing platform, through the Web integration technology, regional information are integrated to the sharing information platform. Regional information center establish their own expert system, the basic information collection and release system, crop condition monitoring system and basic agriculture science and technology consultation system. The remote diagnosis, system information collection and release system, remote learning and training system and single sign-on system are established in the sharing platform.

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Application Expert systems

layer

clien t

Remote diagnosis system

Distance learning & training systems

Information collection release systems

SSO system

The information sharing plantform The front –end

information

process

plantform

Information sharing platform

Information collection platform

Information query platform

Intelligent information classifiction

SSO

Information release platform

Intelligent information search

SSO process platform

Activity analysis mining

The backend information process plantform

Distributed information layer

Agricultural

Information

information

Training

standardation

Policy

information

information

Foundation layer Infrastructural

Information criterion

Information Standardization

Fig. 2. The structure chart of information sharing platform

4.1 Single Sign-On System Single sign-on(SSO) system is mainly for the convenience of sharing platform and the regional information center management, the system architecture is shown in figure 3. Each sharing platform administrator, agricultural science and technology personnel or expert perhaps needs to maintain multiple applications, through the SSO system, he can only need once login to do all of information modification, maintenance, and management of sharing platform and corresponding regional information center.


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Login request Authentication

Token

server(AS)

Client

Result return

platform

嗫

Application

Regional information

嗫

URL request proxy server

Regional information platform

Fig. 3. SSO system architecture

The system is based on Web Service Structure. Each regional basic information platform must register its restricted access application system and set an application proxy in single sign-on system. The proxy contains a registration information table with the URL of the registration application system, replaces user to communicate with the application. The proxy and the basic information platform share a key for secure communications. Each user who needs to access restricted access application must register in single sign-on system, except the identity information, also he must register the information of applications that he can access. After loginning successful, the system distributes the authorization to user by applying for registration information. The authorization is a token form. Once user obtains the authorization, in token validity, he can access the corresponding application, without needing to relogin. 4.1.1 The Design of Users' Data Structures As the uers’ information database is used to store many important information, such as the user accounts and passwords, etc, it is vulnerable to attack. In designing, 3 key data tables are used to ensure the database’s security, such as user basic information table(UserInfo),user token information table(Token) and user agent data table(URLAgent),they are shown in table 1, table 2 and table 3. Table 1. UserInfo

Field names

Description

Remarks

User_ID User_name User_pw User_Role

the unique user ID User name Password User role

Primary key Encypted by MD5 Foreign key

Table 2. Token

Field names

Description

Remarks

SessID UserID CurrentIP ExpireTime

Token ID User name User IP address Termination time of user session

Primary key Foreign key

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Table 3. URLAgent

Field names

Description

Remarks

URLAgentID User_ID URL

URLAgent ID the unique identifier of user The acquisition URL

Primary key Foreign key Encypted by DES

POST HEADER REFER DESURL

HTTPPOST data HTTP header data ReferURL Destination URL

Encypted by DES Encypted by DES Encypted by DES Encypted by DES

4.1.2 The Design of Login Certification Process The client is realized by plug-in, IE browser and Windows resource management are supported. It uses the Web Service SOAP protocol to interact with authentication server(AS). User login certification process is as follows: 1)The client enables HTTPS to connect the AS, and then sends the user name and password to AS by SOAP. 2) After receiving the information, the AS computes the password with MD5, then matches the result with the corresponding encrypted information in database. If they are mismatching, the AS refuses the login request, else, the AS sends an authentication token and URL field data(called URLCache List) to the client. The URLCache List is extracted from URLAgent table according to User_ID. 3) The AS inserts a new record into Token table. The record consists of SessID, UserID, CurrentIP and ExpireTime. Through the above steps, the user logins successfully. Then through the URL of URLCache List, he can access the corresponding service directly. In token validity, he does not need to relogin. 4.2 The Independently Developing System 4.2.1 Expert System Expert system is an intelligent information system with characteristic agriculture domain expert-level knowledge and experience. Since most farmers are lack of computer application skill, the system adopts question and mouse-click means to interact with users, according to mutual information, it can carry out the corresponding intelligent decision-making for users. In daily agricultural production, once insect appears, farmers can easily use the expert system to obtain the corresponding solved methods and skills. 4.2.2 Remote Diagnosis System

If the user cannot obtain satisfactory result by expert system, they can try the remote interactive diagnostic with technical personnel or agricultural experts through the remote diagnosis system. The system is used video transmission to implement, which has


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2 ways of off-line diagnosis and online diagnosis. For off-line diagnosis, users can use photograph, text, pictures and graphics to collect information, and send the messages to the remote diagnostic system. After receiving the messages, system classifies them and submits them to the corresponding technical personnel or expert. When expert offers the solution, system will answer back to the user. For online diagnosis, using video interaction method to implement instant communication, users can interact with experts by using voice, text, video, images and graphics. 4.2.3 System Information Collection and Release System Information collection and release system is divided into 2 parts, the regional information center information collection and release system and the sharing platform of information collection and release system. The regional information center information collection and release system is mainly used to collect the information of crop growth, environment and agricultural products by artificial method or acquisition terminal, and the regional central database gathers, analysis, processing and issuing the information. Users can monitor crops state through the regional central Web. The sharing platform information collection and release system mainly includes 2 functions. One is using "link" search technology to gather products information from the regional information center, then classify and release them to the characteristic agriculture exhibition hall of sharing platform. The other is using resources location information retrieval technology and Web mining technology to collect the information of product and market demand, then classifying, analyzing, processing and release, and realize the subscription to users. 4.2.4 Distance Learning and Training System Distance learning and training system offers video courseware and real-time teaching services for users. Usually, the user can use video courseware to learn agricultural technology intuitively. If there is any technical training, remote training can be held through the real-time teaching system. Real-time teaching system is a multicast real-time teaching system, which is adopted with MPEG4 standard and video compression technology of H.264 standard, based on T.120 standards to develop and implement. It can also withstand over 500 user terminals online class at the same time, and provide many functions, such as the audio and video interacting, whiteboard sharing, document sharing, collaborative browsing etc.

5 Scheme Performance Analysis The scheme has higher communication performance and security performance of network communication. 5.1 Network Performance The sharing platform is based on city telecom broadband network, which is a distributed network system structure. As most access businesses occur in regional information

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center, the traffic is effectively reduced on the information sharing platform, it does not cause an access bottleneck in sharing platform. 5.2 Security Performance On the network boundary, firewall is configured, which can be effectively against the denial of service attacks. The access control by security certification can be effectively defense unauthorized access. Between the single sign-on system and regional information center, shared key is used to implement symmetric encryption communication, which can ensure confidentiality of information.

6 Conclusion Aiming at Zigong characteristic agriculture production and marketing demand, this comprehensive information sharing platform is designed, which is planned to complete within 3 years. So far, the constructions of the city information center agricultural information network and the basic information platform of the black goat breeding center of construction have completed. With this scheme achievement and application, it will strongly promote the city characteristic agricultural development with intensification, scale and commercialization, improve agricultural production efficiency and benefit, and speed up the agricultural informatization construction.

References 1. 2. 3. 4. 5. 6.

7.

Ye, L., Luo, M., Chen, J.-h.: Construction of Agriculture Information Service System for Midland Amountainous Area. J. Computer and Modernization 171(11), 169–171 (2009) Single sign-on, http://en.wikipedia.org/wiki/Single_sign-on Yang, Z., Chen, X.-y., Zhang, B.: A single sign-on scheme supporting double authentication method. J. Computer Applications 27(3), 595–596 (2007) Wu, Q.: Design of Grid Agricultural Information Service System. J. Hubei Agricultural Sciences 48(8), 1998–2000 (2009) Hu, C.-x.: The Effects of agro-informationon Building Socialist New Countryside and Development Strategy. J. Commercial Research 367(11), 131–134 (2007) Liu, X.-h., Zhang, Z.: Experience and Countermeasures of Promoting Regional Agricultural Economy Development by Agricultural Informationization Construction. J. Agriculture Network Information 11, 48–50, 60 (2009) Chen, P., Diao, H.-j., Zhu, F.: A Wed Based System of Single Sign-On. J. Computer Applications and Software 24(11), 147–149 (2007)

Detection of Surface Defects of Fruits Based on Fractal Dimension Yongxiang Sun, Yong Liang, and Qiulan Wu School of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong Province, P.R. China, 271018 [email protected]

Abstract. As the identification of surface defects is very important in fruit automatic detection, a new method for the detection of fruit surface defects based on fractal dimension is suggested. In this method, fruit image was collected using computer vision system. The fractal dimension of fruit image was calculated by an improved ‘box dimension’. The fruit fractal dimension reflects the three dimensional characteristics of the fruit as well as information of the fruit surface. The detection of surface defects of fruits was performed according to a given threshold of fruit image fractal dimension. The results on Fuji apple fruits showed that the improved ‘box dimension’ method was effective and reliable in the detection of fruit defects for its improvement in the accuracy in the calculation of the fractal dimension. Keywords: Fruit; Surface defects; Detection; Fractal dimension.

1 Introduction Although much progress has been made in fruit automatic grading world widely, the detection of fruit surface defects was the most difficult and became one of the limiting factors. [1] In recent years, the development of the theory of fractal provides a new way for the detection of fruit surface defects.[2, 3] Fractal theory is an important branch of nonlinear scientific that gained much attention, which focuses on objects with irregular shape in nonlinear systems in nature. As fractal geometry has many advantages in describing and analyzing the chaotic, irregular and random phenomenon in nature in comparison with traditional geometry, fractal theory was widely used in mathematics, physics, chemistry, material science, biology, medicine, geography, earthquake, astronomy, computer science, and so on. In particularly, in computer science, the ideas and methods of fractal have been made much success in pattern recognition, natural images simulation, and signal processing.[4] In this paper, the detection of fruit defects was successfully performed by analyzing the fractal feature of the fruit image, which is obtained from a computerized image processing system. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 547–554, 2011. © IFIP International Federation for Information Processing 2011

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2 The Calculation of Fractal Dimension of Fruit Image The fractal dimension represents the irregularities of an object, reflecting the shape as well as the surface characteristics of geometric solids. At present, there are several methods concerning the calculation of fractal dimension, in which the ‘box dimension’ became one of the most widely used as it can be easily performed in computer.[5] 2.1 The Traditional Method for the Calculation of ‘Box Dimension’ Let F be a nonempty finite subset of R, Nδ(F) be the amounts of the boxes covering F which has the maximum diameter δ.[6] The lower and upper box dimension of F could be expressed as in formula (1) and (2), respectively.

dim B F = lim

log N δ ( F ) − log δ

(1)

dim B F = lim


(2)

δ →0 +

δ →0−

If (1) and (2) are equal, the box dimension of F could be expressed as in formula (3).

dim B F = lim δ →0


(3)

In fact, the box dimension of F could be considered to be the increasing logarithmic ratios when δ→0, which can be estimated by the slope of logNδ(F) and -logδ. The calculation of box dimensions could be described as follows.[7] A gray image could be considered to be a three-dimensional space (x, y, z), z being the gray values of the pixel (x, y). Thus, a three-dimensional curved surface can be formed with the total three-dimensional pixels of the gray image, which can be considered to be a nonempty finite subset F in the three-dimensional space. In the three-dimensional space, cube boxes of size r×r×r are piled in the x, y, z directions. If these boxes are enough to cover the whole curved surface of the gray image, the amounts of boxes that intersect with the curved surface of the image should be Nδ(F) (Figure 1). Nδ(F) can be calculated as follows. The M × M gray image is divided into grids of r×r size in the x - y plane (1< r ≤ M /2, r being an integer), with a cube box of r×r×r size existing in each grid. Let the minimum and maximum gray value of the image in the (i, j) grid lie in the k box and the l box from bottom-up, respectively, there comes formula (4). nr(i, j) =l- k+1

(4)

In formula (4), nr(i, j) is the amounts of boxes that is needed to cover the image of the (i, j) grid, and Nr is the total boxes covering the whole image, which can be expressed in formula (5). Nr =

∑ n (i, j ) r

i, j

(5)

Detection of Surface Defects of Fruits Based on Fractal Dimension

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According to formula (5), different values of Nr can be obtained with different values of r. Thus, the slope of (log (N r),-log (r)), namely the box dimension D, can be fitted using linear least squares regression. z y

x Fig. 1. The traditional box dimension

In computer, the algorithm for the calculation of the fractal dimension of a gray image is as follows. i. Binarization of the image. Namely, the value of matrix elements of the image is let to be either 1 or 0. ii. Partition of the binary image. Within each parts of the image, its row = column=K(K=1 2 4 … 2i). Thus the image is partitioned into parts of 2i×2i, 2i-1×2i-1, 2i-2×2i-2 …… 21×21 20×20, 2i ≤ the length of the image. iii. Calculation of the pixels. The amounts of image parts that contain the pixel “1” are calculated which is denoted as NK , Thus a serious of values N1, N2, ……, N(i+1) as well as data pairs (K, NK) with the amounts of (i +1), are obtained. iv. Curve fitting. A straight line can be obtained using least squares method (-logK, logNK). v. Calculation of the fractal dimension of the image. The slope of the fitted line, namely the fractal dimension of the image, was determined.

，，，，，，，

In this method, as all of the cubes are located in a fixed position in the calculation of amounts of boxes Nδ(F), there are some disadvantages in the calculation of box dimensions. For example, there are situations that although the curves of some image surfaces are slight, they cover between two cubes. Therefore, the amounts of boxes covering this kind of surface are even more than the amounts of boxes covering image surfaces with larger curves. In this situation, there are some boxes that are not in set F. In the condition of δ→0, this effect on the calculation of box dimension is negligible. But for digital images, δ is not always very small. In this condition, the calculation of box dimensions is affected as the surface is not totally covered by cubes. This situation might get even worse with even larger δ values. 2.2 An Improved Method for the Calculation of ‘Box Dimension’ To solve this problem, an improved method for the calculation of box dimensions was suggested in this paper, which eliminated the effects of ‘empty boxes’ in comparison

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with the traditional method, by covering the image surface with the least amount of δcubes. In the improved method, the cubes are not confined to a fixed position; instead, they can move along the z axis.[8] In this method, great improvement was achieved in the calculation of box dimensions due to reduced amounts of boxes as well as improvement in the tightness that the boxes cover the image surface. The essence of this improved method is that the cuboids of size r×r×r' with variable heights are adopted to cover the image surface instead of the fixed-size cubes of size r×r×r. This method does not disobey the definition of box dimension; instead, it approaches even more close to the essence of the definition of box dimension in comparison with the traditional method for the improvement in the tightness that the boxes cover the image surface (Figure 2). z y

x

Fig. 2. An improved box dimension

The realization process is as follows. The M×M gray image is partitioned into grids of r×r size in the x-y plane (M 1 /3 ≤r≤M /2, r being an integer). Each grid encloses a series of boxes of r×r×r′ size, whose height r′ is a variable, as shown in Figure 2. i. Calculation on the amounts of boxes within the (i, j) grid. Firstly, the serial number of the boxes in each pixel is tracked and scanned within the (i, j) grid; secondly, the gray value of each pixel is calculated within the (i, j) grid; finally, the statistical result, namely the set of indexi, j, can be obtained. ii. Scan of the set indexi, j. In the scanning, elements that appear only once are totally adopted while elements that appear more than once are considered to be the same. A new set indexnew is used to record the serial number of different boxes. The set indexnew can be expressed in formula (6). Indexnew={indexi, j (1),indexi, j (2),…,indexi, j(Q)}

(6)

iii. Let the amounts of elements in Indexnew be Q. It means that in the (i, j) grid, the amounts of boxes that cover curved surface of the image is nr( i, j) =Q. Thus the amounts of boxes in all the grids are the sum of the amounts of boxes in each grid. It could be expressed in formula (5). iv. Finally, the value of Nr with different values of r could be calculated according to formula (5), and thus the fractal dimension D of the image can be obtained according to formula (7). D = log Nr / log(1/r)

(7)


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However, it should be pointed out that much costs would be needed in the calculation of the amounts of boxes in the (i, j) grid in comparison with the traditional method, which should be resolved to work more efficiently in this algorithm.

3 Detection of Surface Defects of Fruits Based on Fractal Dimension This improved algorithm of differential box-counting can be used in fruit surface defects detection. Firstly, fruit image was collected and preprocessed (including graying, noise filtering). Secondly, an appropriate threshold was set, and then removal of the background and binarization of the isolated fruit image was performed.[9] Thirdly, fractal dimension D of the fruit image was calculated using the improved box dimension algorithm. Finally, the threshold of fractal dimension Dthd was determined according to different kinds of fruits, which is used to determine whether the fruit has defects or not. The flow chart of this process is shown in Figure 3. Fruit image acquisition and preprocessing (graying, noise filtering)

Removal of the background and binarization of the fruit image

Calculation of the fractal dimension D using differential box-counting algorithm

No

For a given Dthd, D≥Dthd,? Yes

Defective

Normal

Fig. 3. Detection of fruit surface defects based on fractal dimension

4 Detection of Surface Defects in Fuji Apple Fruits To test whether this method is reliable in fruit defects detection, 50 Fuji apple fruits that have surface defects (defective fruits) and 50 normal fruits were selected. The experimental device was shown in Figure 4. The image acquisition card is DH-CG410 (Daheng Company, China). The image size is 512 × 512 pixels. CCD camera is WV-CP230 (Panasonic).

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4

4

3

1

5 2

Fig. 4. Detection system configuration 1. Light box 2. Sample 3.CCD camera 4. Light sources 5. Computer (inside image acquisition card)

In order to reflect the entire surface of the fruit and to eliminate the effect of position and directions of the fruit on the detection results, eight images were collected for each fruit from random directions. Totally 800 fruit images were collected, which were numbered by letters plus numbers. For example, the 8 images of sample 1 were numbered as A1, B1, C1, D1, E1, F1, G1, and H1, respectively. The representative gray images of 6 samples were shown in Figure 5 and the fractal dimensions derived from this improved method were shown in Table 1.

Fig. 5. Gray image of 6 Fuji apple fruits

Table 1 shows that for the same sample, there was no much difference between the maximum and the minimum box dimensions, indicating that this improved boxcounting method was reliable. The fractal dimension threshold Dthd is set to be 1.30 based on this experiment. According to this value, the detection results of the selected 100 apples are shown in Table 2.


553

Table 1. Fractal dimensions of 6 Fuji apples

Samples

Normal fruits

Defective fruits

1

2

3

4

5

6

1.186 1.073 1.129 1.129 1.129 1.073 1.126 1.188 1.188 1.073

1.299 1.291 1.261 1.284 1.284 1.261 1.299 1.269 1.299 1.261

1.178 1.117 1.132 1.142 1.142 1.144 1.148 1.175 1.178 1.117

1.355 1.387 1.304 1.372 1.304 1.355 1.372 1.371 1.387 1.304

1.412 1.358 1.435 1.450 1.404 1.412 1.400 1.341 1.450 1.341

1.499 1.438 1.456 1.491 1.496 1.538 1.456 1.444 1.538 1.438

0.115

0.038

0.060

0.083

0.109

0.100

1.129

1.281

1.147

1.352

1.401

1.477

Images A B C D E F G H Maximum Minimum Difference between maximum and minimum Mean of each fruit Mean between normal and defective fruits

1.186

1.410

Table 2. Detection results of selected 100 Fuji apples

Samples

Results

Accuracy(%)

rmal fruits (50) Defective fruits (50• Average accuracy(%)

48 49

96.0 98.0 97.0

5 Conclusion and Discussion This method of surface defects detection on fruits based on fractal dimension provides a new way in fruit automatic detection. The fractal dimension of the fruit image reflects the defective area as well as the characteristics of its spatial distribution. The experimental results with Fuji apples showed that this algorithm was effective in the identification of fruit defects. In practice, a database that contains the fractal characteristics of different kinds of fruits should be established. It should be pointed out that

554


the effects of fruit stem on the detection results in this method should be considered in future research.[10]

References 1. Blasco, J., Aleixos, N., Moltó, E.: Machine vision system for automatic quality grading of fruit. Biosystems Engineering 85(4), 415–423 (2003) 2. Nirupam, S., Chaudhuri, B.B.: An efficient differential box-counting approach to compute fractal dimension of image. IEEE Trans. SMC 24(1), 115–120 (1994) 3. Njoroge, J.B., Ninomiya, K., Kondo, N., et al.: Automated fruit grading system using image processing. In: Proceedings of the 41st SICE Annual Conference, SICE 2002, August 5-7, pp. 1346–1351 (2002) 4. Falconer, K.J.: Techniques in Fractal Geometry. John Wiley and Sons Ltd., Chichester (1996) 5. Xie, H., Wang, J.A.: Direct Fractal Measurement of Fracture Surfaces. Int. J. Solids & Structures 36, 3073–3084 (1999) 6. Chaudhuri, B.B., Sarkar, N.: Nirupam Sarkar: Texture segmentation using fractal dimension. Trans. on Pattern Analysis and Machine Intelligence 17(1), 72–77 (1995) 7. Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based feature distributions. Pattern Recognition 29(1), 51–59 (1996) 8. Zhang, T., Yang, Z.B., Huang, A.M.: Improved Extracting Algorithm of Fractal dimension of Remote Sensing Image. Journal of Ordnance Engineering College 18(5), 61–65 (2006) (in Chinese) 9. Lin, K.Y., Wu, J.H., Xu, L.H.: Separation approach for shape grading of fruits using computer vision. Transactions of the CSAM 36(6), 71–74 (2005) (in Chinese) 10. Cai, J.R., Xu, Y.M.: Identification and classification of apple shape based on active shape models. Transactions of the CSAE 22(6), 123–126 (2006) (in Chinese)

Detection Technology for Precision Metering Performance of Magnetic-Type Seeder Based on Machine Vision Deyong Yang1,2, Jianping Hu2, and Zuqing Xie2 1

School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, P.R. China 212013 2 Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University, Zhenjiang, Jiangsu Province, P.R. China 212013 [email protected]

Abstract. Magnetic-type seeder is a precision metering device, particularly suitable for small seeds. A visual inspection system for precision performance of magnetic-type seeder was established based on machine vision and image processing technology. By using pre-treatment techniques including image binarization and image filtering, image quality was enhanced effectively. As the grayscale value of the seeds coated with magnetic powder is very close to the electromagnet, the method of seed feature extraction based on morphological image processing is proposed and performance testing model of precision metering seed is put forward. The results of comparison between machine vision and manual detection showed that the relative error of preciseness was less than 3% and coefficient of variation and standard deviation were less than 5%, which indicated the system is of high accuracy when used in real-time detection. Keywords: precision metering, performance detection, magnetic-type seeder, machine vision.

1 Introduction Metering device is the core component of seed planters, which performance is important for planter design and manufacture. The performance improvement of precision metering device depends on accurate and efficient detection technology. There were manual detection, opto-electronic scanning [1], piezoelectric pulse and high-speed photography [2] and other methods. In the recent years, the research of non-contact detection of precision planting quality using machine vision technology conducted gradually [3],[4],[5],[8]. Magnet roller-type precision seeder was developed according to magnetic seed-metering principle, which solve precision seeding of small seed like vegetable seeds and flower seeds [6].In this research, vision detecting of precision performance of magnetic-type seed metering device was undertaken, and visual detection accuracy was discussed. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part I, IFIP AICT 344, pp. 555–562, 2011. © IFIP International Federation for Information Processing 2011

556

D. Yang, J. Hu, and Z. Xie

2 Materials and Methods 2.1 Seed Rape seed of 2.412 g /1000 seeds was used for this study. Seeds must be coated with magnetic powder. 2.2 The Vision Detection System A test stand with camera system was used to detect performance of precision metering device, as shown in Fig.1 and Fig.2. Magnetic-type seed metering device has 4 rows magnetic head per revolution. Sufficient oil was added to the top surface of the seed-bed belt to capture the seed as it was released from metering device. The speeds of the metering roller were set at 15, 20 and 30 rpm while the speed of seed-bed belt was 0.5 m/s. The camera is a high-resolution, black-and-white, low-light, manual gain adjustment MTV-1881EX equipped with AVENIR Seiko CCTV lens, manual iris of F1.3, focal length of 8mm. Lighting system is made up of the lighting box, 12V DC lamps and blocking mask, and 6 lamps are fixed below the box, and the camera and lighting are arranged hierarchically to obtain stable, no shadow and uniform illumination image to meet the needs of image processing. As seed-bed belt moved at a constant speed, seeds from the metering device fall onto the moving belt and were sticked on the belt. With the movement of the belt, seeds go through the camera and the camera records images and collect data, then transfer into the computer for processing.

1 screen filter and fuel tank 2 driving roller 3 seed-bed motor 4 seed-bed belt 5 camera system 6 metering motor 7 magnetic-type seeder 8 fuel injection device 9 driven roller Fig. 1. Structural diagram of precision seeder test-bed

Fig. 2. Photograph of precision seeder test-bed

Detection Technology for Precision Metering Performance of Magnetic-Type Seeder

557

2.3 Image Processing Because the images in the collection and transmission process were interfered by all sorts of disturbance and contain random noise in addition to images useful signal, the images must be treated by a series of methods, such as median filtering and image splicing, to remove the noise and make seed target and background separate completely. 2.3.1 Binary Image Processing In seed image acquisition process, relatively stable reservoir thickness, viscosity, seeds, colour and lustre etc are likely to make gray-scale values of image pixel except seeds are 1. Therefore, seeds are separated with background by using binary processing. The background in this research is simple and few changed, so a preprocessing fixed threshold method was tried, namely, by setting a certain threshold, make gray scale image into two gray value of black-and-white images, which would target and background separated. Its function expression is as follows: ⎧0 f ( x) = ⎨ ⎩1

x

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